/usr/lib/python2.7/dist-packages/pymc/InstantiationDecorators.py is in python-pymc 2.2+ds-1.1.
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The decorators stochastic, deterministic, discrete_stochastic, binary_stochastic, potential and data
are defined here, but the actual objects are defined in PyMCObjects.py
"""
__all__ = ['stochastic', 'stoch', 'deterministic', 'dtrm', 'potential', 'pot', 'data', 'observed', 'robust_init','disable_special_methods','enable_special_methods','check_special_methods']
import sys, inspect, pdb
from imp import load_dynamic
from .PyMCObjects import Stochastic, Deterministic, Potential
from .Node import ZeroProbability, ContainerBase, Node, StochasticMeta
from .Container import Container
import numpy as np
special_methods_available = [True]
def disable_special_methods(sma=special_methods_available):
sma[0]=False
def enable_special_methods(sma=special_methods_available):
sma[0]=True
def check_special_methods(sma=special_methods_available):
return sma[0]
from . import six
def _extract(__func__, kwds, keys, classname, probe=True):
"""
Used by decorators stochastic and deterministic to inspect declarations
"""
# Add docs and name
kwds['doc'] = __func__.__doc__
if not 'name' in kwds:
kwds['name'] = __func__.__name__
# kwds.update({'doc':__func__.__doc__, 'name':__func__.__name__})
# Instanitate dictionary of parents
parents = {}
# This gets used by stochastic to check for long-format logp and random:
if probe:
cur_status = check_special_methods()
disable_special_methods()
# Define global tracing function (I assume this is for debugging??)
# No, it's to get out the logp and random functions, if they're in there.
def probeFunc(frame, event, arg):
if event == 'return':
locals = frame.f_locals
kwds.update(dict((k,locals.get(k)) for k in keys))
sys.settrace(None)
return probeFunc
sys.settrace(probeFunc)
# Get the functions logp and random (complete interface).
# Disable special methods to prevent the formation of a hurricane of Deterministics
try:
__func__()
except:
if 'logp' in keys:
kwds['logp']=__func__
else:
kwds['eval'] =__func__
# Reenable special methods.
if cur_status:
enable_special_methods()
for key in keys:
if not key in kwds:
kwds[key] = None
for key in ['logp', 'eval']:
if key in keys:
if kwds[key] is None:
kwds[key] = __func__
# Build parents dictionary by parsing the __func__tion's arguments.
(args, varargs, varkw, defaults) = inspect.getargspec(__func__)
if defaults is None:
defaults = ()
# Make sure all parents were defined
arg_deficit = (len(args) - ('value' in args)) - len(defaults)
if arg_deficit > 0:
err_str = classname + ' ' + __func__.__name__ + ': no parent provided for the following labels:'
for i in range(arg_deficit):
err_str += " " + args[i + ('value' in args)]
if i < arg_deficit-1:
err_str += ','
raise ValueError(err_str)
# Fill in parent dictionary
try:
parents.update(dict(zip(args[-len(defaults):], defaults)))
except TypeError:
pass
value = parents.pop('value', None)
return (value, parents)
def stochastic(__func__=None, __class__=Stochastic, binary=False, discrete=False, **kwds):
"""
Decorator function for instantiating stochastic variables. Usages:
Medium:
@stochastic
def A(value = ., parent_name = ., ...):
return foo(value, parent_name, ...)
@stochastic(trace=trace_object)
def A(value = ., parent_name = ., ...):
return foo(value, parent_name, ...)
Long:
@stochastic
def A(value = ., parent_name = ., ...):
def logp(value, parent_name, ...):
return foo(value, parent_name, ...)
def random(parent_name, ...):
return bar(parent_name, ...)
@stochastic(trace=trace_object)
def A(value = ., parent_name = ., ...):
def logp(value, parent_name, ...):
return foo(value, parent_name, ...)
def random(parent_name, ...):
return bar(parent_name, ...)
where foo() computes the log-probability of the variable A
conditional on its value and its parents' values, and bar()
generates a random value from A's distribution conditional on
its parents' values.
:SeeAlso:
Stochastic, Deterministic, deterministic, data, Potential, potential, Model,
distributions
"""
def instantiate_p(__func__):
value, parents = _extract(__func__, kwds, keys, 'Stochastic')
return __class__(value=value, parents=parents, **kwds)
keys = ['logp','random','rseed']
instantiate_p.kwds = kwds
if __func__:
return instantiate_p(__func__)
return instantiate_p
# Shortcut alias
stoch = stochastic
def potential(__func__ = None, **kwds):
"""
Decorator function instantiating potentials. Usage:
@potential
def B(parent_name = ., ...)
return baz(parent_name, ...)
where baz returns the deterministic B's value conditional
on its parents.
:SeeAlso:
Deterministic, deterministic, Stochastic, Potential, stochastic, data, Model
"""
def instantiate_pot(__func__):
junk, parents = _extract(__func__, kwds, keys, 'Potential', probe=False)
return Potential(parents=parents, **kwds)
keys = ['logp']
instantiate_pot.kwds = kwds
if __func__:
return instantiate_pot(__func__)
return instantiate_pot
pot = potential
def deterministic(__func__ = None, **kwds):
"""
Decorator function instantiating deterministic variables. Usage:
@deterministic
def B(parent_name = ., ...)
return baz(parent_name, ...)
@deterministic(trace = trace_object)
def B(parent_name = ., ...)
return baz(parent_name, ...)
where baz returns the variable B's value conditional
on its parents.
:SeeAlso:
Deterministic, Potential, potential, Stochastic, stochastic, data, Model,
CommonDeterministics
"""
def instantiate_n(__func__):
junk, parents = _extract(__func__, kwds, keys, 'Deterministic', probe=False)
return Deterministic(parents=parents, **kwds)
keys = ['eval']
instantiate_n.kwds = kwds
if __func__:
return instantiate_n(__func__)
return instantiate_n
# Shortcut alias
dtrm = deterministic
def observed(obj=None, **kwds):
"""
Decorator function to instantiate data objects.
If given a Stochastic, sets a the observed flag to True.
Can be used as
@observed
def A(value = ., parent_name = ., ...):
return foo(value, parent_name, ...)
or as
@stochastic(observed=True)
def A(value = ., parent_name = ., ...):
return foo(value, parent_name, ...)
:SeeAlso:
stochastic, Stochastic, dtrm, Deterministic, potential, Potential, Model,
distributions
"""
if obj is not None:
if isinstance(obj, Stochastic):
obj._observed=True
return obj
else:
p = stochastic(__func__=obj, observed=True, **kwds)
return p
kwds['observed']=True
def instantiate_observed(func):
return stochastic(func, **kwds)
return instantiate_observed
data = observed
def robust_init(stochclass, tries, *args, **kwds):
"""Robust initialization of a Stochastic.
If the evaluation of the log-probability returns a ZeroProbability
error, due for example to a parent being outside of the support for
this Stochastic, the values of parents are randomly sampled until
a valid log-probability is obtained.
If the log-probability is still not valid after `tries` attempts, the
original ZeroProbability error is raised.
:Parameters:
stochclass : Stochastic, eg. Normal, Uniform, ...
The Stochastic distribution to instantiate.
tries : int
Maximum number of times parents will be sampled.
*args, **kwds
Positional and keyword arguments to declare the Stochastic variable.
:Example:
>>> lower = pymc.Uniform('lower', 0., 2., value=1.5, rseed=True)
>>> pymc.robust_init(pymc.Uniform, 100, 'data', lower=lower, upper=5, value=[1,2,3,4], observed=True)
"""
# Find the direct parents
stochs = [arg for arg in (list(args) + list(kwds.values())) \
if isinstance(arg.__class__, StochasticMeta)]
# Find the extended parents
parents = stochs
for s in stochs:
parents.extend(s.extended_parents)
extended_parents = set(parents)
# Select the parents with a random method.
random_parents = [p for p in extended_parents if p.rseed is True and hasattr(p, 'random')]
for i in range(tries):
try:
return stochclass(*args, **kwds)
except ZeroProbability:
exc = sys.exc_info()
for parent in random_parents:
try:
parent.random()
except:
six.reraise(*exc)
six.reraise(*exc)
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