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May 18, 2018
Merged

Bug Fix for 2909 #2979

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38 changes: 19 additions & 19 deletions pymc3/distributions/continuous.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,7 +163,7 @@ def __init__(self, lower=0, upper=1, transform='interval',

def random(self, point=None, size=None):
lower, upper = draw_values([self.lower, self.upper],
point=point)
point=point, size=size)
return generate_samples(stats.uniform.rvs, loc=lower,
scale=upper - lower,
dist_shape=self.shape,
Expand Down Expand Up @@ -748,7 +748,7 @@ def __init__(self, lam, *args, **kwargs):
assert_negative_support(lam, 'lam', 'Exponential')

def random(self, point=None, size=None):
lam = draw_values([self.lam], point=point)[0]
lam = draw_values([self.lam], point=point, size=size)[0]
return generate_samples(np.random.exponential, scale=1. / lam,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -817,7 +817,7 @@ def __init__(self, mu, b, *args, **kwargs):
assert_negative_support(b, 'b', 'Laplace')

def random(self, point=None, size=None):
mu, b = draw_values([self.mu, self.b], point=point)
mu, b = draw_values([self.mu, self.b], point=point, size=size)
return generate_samples(np.random.laplace, mu, b,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -921,7 +921,7 @@ def _random(self, mu, tau, size=None):
return np.exp(mu + (tau**-0.5) * samples)

def random(self, point=None, size=None):
mu, tau = draw_values([self.mu, self.tau], point=point)
mu, tau = draw_values([self.mu, self.tau], point=point, size=size)
return generate_samples(self._random, mu, tau,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -1023,7 +1023,7 @@ def __init__(self, nu, mu=0, lam=None, sd=None, *args, **kwargs):

def random(self, point=None, size=None):
nu, mu, lam = draw_values([self.nu, self.mu, self.lam],
point=point)
point=point, size=size)
return generate_samples(stats.t.rvs, nu, loc=mu, scale=lam**-0.5,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -1121,7 +1121,7 @@ def _random(self, alpha, m, size=None):

def random(self, point=None, size=None):
alpha, m = draw_values([self.alpha, self.m],
point=point)
point=point, size=size)
return generate_samples(self._random, alpha, m,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -1202,7 +1202,7 @@ def _random(self, alpha, beta, size=None):

def random(self, point=None, size=None):
alpha, beta = draw_values([self.alpha, self.beta],
point=point)
point=point, size=size)
return generate_samples(self._random, alpha, beta,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -1276,7 +1276,7 @@ def _random(self, beta, size=None):
return beta * np.abs(np.tan(np.pi * (u - 0.5)))

def random(self, point=None, size=None):
beta = draw_values([self.beta], point=point)[0]
beta = draw_values([self.beta], point=point, size=size)[0]
return generate_samples(self._random, beta,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -1381,7 +1381,7 @@ def get_alpha_beta(self, alpha=None, beta=None, mu=None, sd=None):

def random(self, point=None, size=None):
alpha, beta = draw_values([self.alpha, self.beta],
point=point)
point=point, size=size)
return generate_samples(stats.gamma.rvs, alpha, scale=1. / beta,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -1474,7 +1474,7 @@ def _calculate_mean(self):

def random(self, point=None, size=None):
alpha, beta = draw_values([self.alpha, self.beta],
point=point)
point=point, size=size)
return generate_samples(stats.invgamma.rvs, a=alpha, scale=beta,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -1610,7 +1610,7 @@ def __init__(self, alpha, beta, *args, **kwargs):

def random(self, point=None, size=None):
alpha, beta = draw_values([self.alpha, self.beta],
point=point)
point=point, size=size)

def _random(a, b, size=None):
return b * (-np.log(np.random.uniform(size=size)))**(1 / a)
Expand Down Expand Up @@ -1708,7 +1708,7 @@ def __init__(self, nu=1, sd=None, lam=None, *args, **kwargs):
assert_negative_support(nu, 'nu', 'HalfStudentT')

def random(self, point=None, size=None):
nu, sd = draw_values([self.nu, self.sd], point=point)
nu, sd = draw_values([self.nu, self.sd], point=point, size=size)
return np.abs(generate_samples(stats.t.rvs, nu, loc=0, scale=sd,
dist_shape=self.shape,
size=size))
Expand Down Expand Up @@ -1813,7 +1813,7 @@ def __init__(self, mu, sigma, nu, *args, **kwargs):

def random(self, point=None, size=None):
mu, sigma, nu = draw_values([self.mu, self.sigma, self.nu],
point=point)
point=point, size=size)

def _random(mu, sigma, nu, size=None):
return (np.random.normal(mu, sigma, size=size)
Expand Down Expand Up @@ -1905,7 +1905,7 @@ def __init__(self, mu=0.0, kappa=None, transform='circular',

def random(self, point=None, size=None):
mu, kappa = draw_values([self.mu, self.kappa],
point=point)
point=point, size=size)
return generate_samples(stats.vonmises.rvs, loc=mu, kappa=kappa,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -2002,7 +2002,7 @@ def __init__(self, mu=0.0, sd=None, tau=None, alpha=1, *args, **kwargs):

def random(self, point=None, size=None):
mu, tau, _, alpha = draw_values(
[self.mu, self.tau, self.sd, self.alpha], point=point)
[self.mu, self.tau, self.sd, self.alpha], point=point, size=size)
return generate_samples(stats.skewnorm.rvs,
a=alpha, loc=mu, scale=tau**-0.5,
dist_shape=self.shape,
Expand Down Expand Up @@ -2095,7 +2095,7 @@ def __init__(self, lower=0, upper=1, c=0.5,

def random(self, point=None, size=None):
c, lower, upper = draw_values([self.c, self.lower, self.upper],
point=point)
point=point, size=size)
return generate_samples(stats.triang.rvs, c=c-lower, loc=lower, scale=upper-lower,
size=size, dist_shape=self.shape, random_state=None)

Expand Down Expand Up @@ -2178,7 +2178,7 @@ def __init__(self, mu=0, beta=1.0, **kwargs):
super(Gumbel, self).__init__(**kwargs)

def random(self, point=None, size=None):
mu, sd = draw_values([self.mu, self.beta], point=point)
mu, sd = draw_values([self.mu, self.beta], point=point, size=size)
return generate_samples(stats.gumbel_r.rvs, loc=mu, scale=sd,
dist_shape=self.shape,
size=size)
Expand Down Expand Up @@ -2257,7 +2257,7 @@ def logp(self, value):
-(value - mu) / s - tt.log(s) - 2 * tt.log1p(tt.exp(-(value - mu) / s)), s > 0)

def random(self, point=None, size=None):
mu, s = draw_values([self.mu, self.s], point=point)
mu, s = draw_values([self.mu, self.s], point=point, size=size)

return generate_samples(
stats.logistic.rvs,
Expand Down Expand Up @@ -2333,7 +2333,7 @@ def __init__(self, mu=0, sd=None, tau=None, **kwargs):
super(LogitNormal, self).__init__(**kwargs)

def random(self, point=None, size=None):
mu, _, sd = draw_values([self.mu, self.tau, self.sd], point=point)
mu, _, sd = draw_values([self.mu, self.tau, self.sd], point=point, size=size)
return expit(generate_samples(stats.norm.rvs, loc=mu, scale=sd, dist_shape=self.shape,
size=size))

Expand Down
2 changes: 1 addition & 1 deletion pymc3/distributions/discrete.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,7 +66,7 @@ def __init__(self, n, p, *args, **kwargs):
self.mode = tt.cast(tround(n * p), self.dtype)

def random(self, point=None, size=None):
n, p = draw_values([self.n, self.p], point=point)
n, p = draw_values([self.n, self.p], point=point, size=size)
return generate_samples(stats.binom.rvs, n=n, p=p,
dist_shape=self.shape,
size=size)
Expand Down
16 changes: 9 additions & 7 deletions pymc3/distributions/distribution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -210,7 +210,7 @@ def random(self, *args, **kwargs):



def draw_values(params, point=None):
def draw_values(params, point=None, size=None):
"""
Draw (fix) parameter values. Handles a number of cases:

Expand Down Expand Up @@ -251,10 +251,10 @@ def draw_values(params, point=None):
named_nodes_children[k] = nnc[k]
else:
named_nodes_children[k].update(nnc[k])

# Init givens and the stack of nodes to try to `_draw_value` from
givens = {}
stored = set([]) # Some nodes
stored = set([]) # Some nodes
stack = list(leaf_nodes.values()) # A queue would be more appropriate
while stack:
next_ = stack.pop(0)
Expand All @@ -269,7 +269,7 @@ def draw_values(params, point=None):
# we can skip it. Furthermore, if this node was treated as a
# TensorVariable that should be compiled by theano in
# _compile_theano_function, it would raise a `TypeError:
# ('Constants not allowed in param list', ...)` for
# ('Constants not allowed in param list', ...)` for
# TensorConstant, and a `TypeError: Cannot use a shared
# variable (...) as explicit input` for SharedVariable.
stored.add(next_.name)
Expand All @@ -285,7 +285,7 @@ def draw_values(params, point=None):
# have the random method
givens[next_.name] = (next_, _draw_value(next_,
point=point,
givens=temp_givens))
givens=temp_givens, size=size))
stored.add(next_.name)
except theano.gof.fg.MissingInputError:
# The node failed, so we must add the node's parents to
Expand All @@ -297,7 +297,7 @@ def draw_values(params, point=None):
node not in params])
values = []
for param in params:
values.append(_draw_value(param, point=point, givens=givens.values()))
values.append(_draw_value(param, point=point, givens=givens.values(), size=size))
return values


Expand Down Expand Up @@ -326,7 +326,7 @@ def _compile_theano_function(param, vars, givens=None):
allow_input_downcast=True)


def _draw_value(param, point=None, givens=None):
def _draw_value(param, point=None, givens=None, size=None):
"""Draw a random value from a distribution or return a constant.

Parameters
Expand All @@ -342,6 +342,8 @@ def _draw_value(param, point=None, givens=None):
givens : dict, optional
A dictionary from theano variables to their values. These values
are used to evaluate `param` if it is a theano variable.
size : int, optional
Number of samples
"""
if isinstance(param, numbers.Number):
return param
Expand Down
14 changes: 14 additions & 0 deletions pymc3/tests/test_distributions_random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,6 +97,20 @@ def test_random_sample_returns_nd_array(self):
assert isinstance(mu, np.ndarray)
assert isinstance(tau, np.ndarray)

def test_random_sample_returns_correctly(self):
# Based on what we discovered in #GH2909
with pm.Model():
a = pm.Uniform('a', lower=0, upper=1, shape=10)
b = pm.Binomial('b', n=1, p=a, shape=10)
array_of_uniform = a.random(size=10000).mean(axis=0)
array_of_binomial = b.random(size=10000).mean(axis=0)
npt.assert_allclose(array_of_uniform, [0.49886929, 0.49949713, 0.49946077, 0.49922606, 0.49927498, 0.50003914,
0.49980687, 0.50180495, 0.500905, 0.50035121], rtol=1e-2, atol=0)
npt.assert_allclose(array_of_binomial, [0.7232, 0.131 , 0.9457, 0.8279, 0.2911, 0.8686, 0.57 , 0.9184,
0.8177, 0.1625], rtol=1e-2, atol=0)
assert isinstance(array_of_binomial, np.ndarray)
assert isinstance(array_of_uniform, np.ndarray)


class BaseTestCases(object):
class BaseTestCase(SeededTest):
Expand Down