/usr/lib/python2.7/dist-packages/pymc/tests/test_gradients.py is in python-pymc 2.2+ds-1.1.
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from pymc import *
from numpy.testing import *
import nose
import sys
from pymc import utils
from pymc import six
import pymc
float_dtypes = [float, single, float_, longfloat]
def generate_model():
# Simulated data
ReData = arange(200, 3000, 25)
measured = 10.2 * (ReData )** .5 + random.normal(scale = 55, size = size(ReData))
sd =pymc.Uniform('sd', lower = 5, upper = 100)
a = pymc.Uniform('a', lower = 0, upper = 100)
b = pymc.Uniform('b', lower = .05, upper = 2.0)
nonlinear = a * (ReData )** b
precision = sd **-2
results = pymc.Normal('results', mu = nonlinear, tau = precision, value = measured, observed = True)
return locals()
def find_variable_set(stochastic):
set = [stochastic]
for parameter, variable in six.iteritems(stochastic.parents):
if isinstance(variable, Variable):
set.append(variable)
return set
def check_jacobians( deterministic):
for parameter, pvalue in six.iteritems(deterministic.parents):
if isinstance(pvalue, Variable):
grad = random.normal(.5, .1, size = shape(deterministic.value))
a_partial_grad = get_analytic_partial_gradient(deterministic, parameter, pvalue, grad)
n_partial_grad = get_numeric_partial_gradient(deterministic, pvalue, grad)
assert_array_almost_equal(a_partial_grad, n_partial_grad,4,
"analytic partial gradient for " + str(deterministic) +
" with respect to parameter " + str(parameter) +
" is not correct.")
def get_analytic_partial_gradient(deterministic, parameter, variable, grad):
try:
jacobian = deterministic._jacobians[parameter].get()
except KeyError:
raise ValueError(str(deterministic) +" has no jacobian for " + str(parameter))
mapping = deterministic._jacobian_formats.get(parameter, 'full')
return deterministic._format_mapping[mapping](deterministic, variable, jacobian, grad)
def get_numeric_partial_gradient( deterministic, pvalue, grad ):
j = get_numeric_jacobian(deterministic, pvalue)
pg = deterministic._format_mapping['full'](deterministic, pvalue,j , grad)
return reshape(pg, shape(pvalue.value))
def get_numeric_jacobian( deterministic, pvalue ):
e = 1e-9
initial_pvalue = pvalue.value
shape = initial_pvalue.shape
size = initial_pvalue.size
initial_value = ravel(deterministic.value)
numeric_jacobian= zeros((deterministic.value.size,size))
for i in range(size):
delta = zeros(size)
delta[i] += e
pvalue.value = reshape(initial_pvalue.ravel() + delta, shape)
value = ravel(deterministic.value)
numeric_jacobian[:, i] = (value - initial_value)/e
pvalue.value = initial_pvalue
return numeric_jacobian
def check_model_gradients( model):
model = set(model)
# find the markov blanket
children = set([])
for s in model:
for s2 in s.extended_children:
if isinstance(s2, Stochastic) and s2.observed == True:
children.add( s2)
# self.markov_blanket is a list, because we want self.stochastics to have the chance to
# raise ZeroProbability exceptions before self.children.
markov_blanket = list(model)+list(children)
gradients = utils.logp_gradient_of_set(model)
for variable in model:
analytic_gradient = gradients[variable]
numeric_gradient = get_numeric_gradient(markov_blanket, variable)
assert_array_almost_equal(numeric_gradient, analytic_gradient,3,
"analytic gradient for model " + str(model) +
" with respect to variable " + str(variable) +
" is not correct.")
def check_gradients( stochastic):
stochastics = find_variable_set(stochastic)
gradients = utils.logp_gradient_of_set(stochastics, stochastics)
for s, analytic_gradient in six.iteritems(gradients):
numeric_gradient = get_numeric_gradient(stochastics, s)
assert_array_almost_equal(numeric_gradient, analytic_gradient,4,
"analytic gradient for " + str(stochastic) +
" with respect to parameter " + str(s) +
" is not correct.")
def get_numeric_gradient( stochastic, pvalue ):
e = 1e-9
initial_value = pvalue.value
i_shape = shape(initial_value)
i_size = size(initial_value)
initial_logp = utils.logp_of_set(stochastic)
numeric_gradient = zeros(i_shape)
if not (pvalue.dtype in float_dtypes):
return numeric_gradient
for i in range(i_size):
delta = zeros(i_shape)
try:
delta[unravel_index(i,i_shape or (1,))] += e
except IndexError:
delta += e
pvalue.value = initial_value + delta
logp = utils.logp_of_set(stochastic)
try:
numeric_gradient[unravel_index(i,i_shape or (1,))] = (logp - initial_logp)/e
except IndexError:
numeric_gradient = (logp - initial_logp)/e
pvalue.value = initial_value
return numeric_gradient
class test_gradients(TestCase):
def test_jacobians(self):
shape = (3, 10)
a = Normal('a', mu = zeros(shape), tau = ones(shape))
b = Normal('b', mu = zeros(shape), tau = ones(shape))
c = Uniform('c', lower = ones(shape) * .1, upper = ones(shape) * 10)
d = Uniform('d', lower = ones(shape) * -10, upper = ones(shape) * -.1)
addition = a + b
check_jacobians(addition)
subtraction = a - b
check_jacobians(subtraction)
multiplication = a * b
check_jacobians(subtraction)
division1 = a / c
check_jacobians(division1)
division2 = a / d
check_jacobians(division2)
a2 = Uniform('a2', lower = .1 * ones(shape), upper = 2.0 * ones(shape))
powering = a2 ** b
check_jacobians(powering)
negation = -a
check_jacobians(negation)
absing = abs(a)
check_jacobians(absing)
indexing1 = a[0:1,5:8]
check_jacobians(indexing1)
indexing3 = a[::-1,:]
check_jacobians(indexing3)
# this currently does not work because scalars use the Index deterministic
# which is special and needs more thought
indexing2 = a[0]
check_jacobians(indexing2)
def test_numpy_deterministics_jacobians(self):
shape = (2, 3)
a = Normal('a', mu = zeros(shape), tau = ones(shape)*5)
b = Normal('b', mu = zeros(shape), tau = ones(shape))
c = Uniform('c', lower = ones(shape) * .1, upper = ones(shape) * 10)
d = Uniform('d', lower = ones(shape) * -1.0, upper = ones(shape) * 1.0)
e = Normal('e', mu = zeros(shape), tau = ones(shape))
f = Uniform('c', lower = ones(shape) * 1.0, upper = ones(shape) * 10)
summing = sum(a, axis = 0)
check_jacobians(summing)
summing2 = sum(a)
check_jacobians(summing2)
absing = abs(a)
check_jacobians(absing)
exping = exp(a)
check_jacobians(exping)
logging = log(c)
check_jacobians(logging)
sqrting = sqrt(c)
check_jacobians(sqrting)
sining = sin(a)
check_jacobians(sining)
cosing = cos(a)
check_jacobians(cosing)
taning = tan(a)
check_jacobians(taning)
arcsining = arcsin(d)
check_jacobians(arcsining)
arcosing = arccos(d)
check_jacobians(arcosing)
arctaning = arctan(d)
check_jacobians(arctaning)
sinhing = sinh(a)
check_jacobians(sinhing)
coshing = cosh(a)
check_jacobians(coshing)
tanhing = tanh(a)
check_jacobians(tanhing)
arcsinhing = arcsinh(a)
check_jacobians(arcsinhing)
arccoshing = arccosh(f)
check_jacobians(arccoshing)
arctanhing = arctanh(d)
check_jacobians(arctanhing)
arctan2ing = arctan2(b, e)
check_jacobians(arctan2ing)
hypoting = hypot(b, e)
check_jacobians(hypoting)
def test_gradients(self):
shape = (5,)
a = Normal('a', mu = zeros(shape), tau = ones(shape))
b = Normal('b', mu = zeros(shape), tau = ones(shape))
b2 = Normal('b2', mu = 2, tau = 1.0)
c = Uniform('c', lower = ones(shape) * .7, upper = ones(shape) * 2.5 )
d = Uniform('d', lower = ones(shape) * .7, upper = ones(shape) * 2.5 )
e = Uniform('e', lower = ones(shape) * .2, upper = ones(shape) * 10)
f = Uniform('f' , lower = ones(shape) * 2, upper = ones(shape) * 30)
p = Uniform('p', lower = zeros(shape) +.05 , upper = ones(shape) -.05 )
n = 5
a.value = 2 * ones(shape)
b.value = 2.5 * ones(shape)
b2.value = 2.5
norm = Normal('norm', mu = a, tau = b)
check_gradients(norm)
norm2 = Normal('norm2', mu = 0, tau = b2)
check_gradients(norm2)
gamma = Gamma('gamma', alpha = a, beta = b)
check_gradients(gamma)
bern = Bernoulli('bern',p = p)
check_gradients(bern )
beta = Beta('beta', alpha = c, beta = d)
check_gradients(beta)
cauchy = Cauchy('cauchy', alpha = a, beta = d)
check_gradients(cauchy)
chi2 = Chi2('chi2', nu = e)
check_gradients(chi2)
exponential = Exponential('expon', beta = d)
check_gradients(exponential)
t = T('t', nu = f)
check_gradients(t)
half_normal = HalfNormal('half_normal', tau = e)
check_gradients(half_normal)
inverse_gamma = InverseGamma ('inverse_gamma', alpha = c, beta = d)
check_gradients(inverse_gamma)
laplace = Laplace('laplace', mu = a , tau = c)
check_gradients(laplace)
lognormal = Lognormal('lognormal', mu = a, tau = c)
check_gradients(lognormal)
weibull = Weibull('weibull', alpha = c, beta = d)
check_gradients(weibull)
binomial = Binomial('binomial', p = p, n = n)
check_gradients(binomial)
geometric = Geometric('geometric', p = p)
check_gradients(geometric)
poisson = Poisson('poisson', mu = c)
check_gradients(poisson)
u = Uniform('u', lower = a, upper = b)
check_gradients(u)
negative_binomial = NegativeBinomial('negative_binomial', mu = c, alpha = d )
check_gradients(negative_binomial)
#exponweib still not working for unknown reasons
#exponweib = Exponweib('exponweib', alpha = c, k =d , loc = a, scale = e )
#check_gradients(exponweib)
def test_model(self):
model = generate_model()
model["sd"].value = 55.0
model["a"].value = 10.2
model["b"].value = .5
check_gradients(model["sd"])
M = pymc.MCMC(model)
check_model_gradients(M.stochastics)
if __name__ == '__main__':
C =nose.config.Config(verbosity=1)
nose.runmodule(config=C)
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