Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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455 lines
17 KiB
455 lines
17 KiB
4 years ago
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# ######################### LICENSE ############################ #
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# Copyright (c) 2005-2018, Michele Simionato
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# All rights reserved.
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met:
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# Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# Redistributions in bytecode form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in
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# the documentation and/or other materials provided with the
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# distribution.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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# HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
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# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
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# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
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# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
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# DAMAGE.
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"""
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Decorator module, see http://pypi.python.org/pypi/decorator
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for the documentation.
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"""
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from __future__ import print_function
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import re
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import sys
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import inspect
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import operator
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import itertools
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import collections
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__version__ = '4.4.2'
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if sys.version_info >= (3,):
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from inspect import getfullargspec
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def get_init(cls):
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return cls.__init__
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else:
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FullArgSpec = collections.namedtuple(
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'FullArgSpec', 'args varargs varkw defaults '
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'kwonlyargs kwonlydefaults annotations')
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def getfullargspec(f):
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"A quick and dirty replacement for getfullargspec for Python 2.X"
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return FullArgSpec._make(inspect.getargspec(f) + ([], None, {}))
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def get_init(cls):
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return cls.__init__.__func__
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try:
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iscoroutinefunction = inspect.iscoroutinefunction
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except AttributeError:
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# let's assume there are no coroutine functions in old Python
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def iscoroutinefunction(f):
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return False
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try:
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from inspect import isgeneratorfunction
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except ImportError:
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# assume no generator function in old Python versions
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def isgeneratorfunction(caller):
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return False
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DEF = re.compile(r'\s*def\s*([_\w][_\w\d]*)\s*\(')
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# basic functionality
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class FunctionMaker(object):
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"""
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An object with the ability to create functions with a given signature.
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It has attributes name, doc, module, signature, defaults, dict and
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methods update and make.
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"""
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# Atomic get-and-increment provided by the GIL
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_compile_count = itertools.count()
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# make pylint happy
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args = varargs = varkw = defaults = kwonlyargs = kwonlydefaults = ()
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def __init__(self, func=None, name=None, signature=None,
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defaults=None, doc=None, module=None, funcdict=None):
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self.shortsignature = signature
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if func:
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# func can be a class or a callable, but not an instance method
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self.name = func.__name__
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if self.name == '<lambda>': # small hack for lambda functions
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self.name = '_lambda_'
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self.doc = func.__doc__
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self.module = func.__module__
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if inspect.isfunction(func):
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argspec = getfullargspec(func)
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self.annotations = getattr(func, '__annotations__', {})
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for a in ('args', 'varargs', 'varkw', 'defaults', 'kwonlyargs',
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'kwonlydefaults'):
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setattr(self, a, getattr(argspec, a))
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for i, arg in enumerate(self.args):
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setattr(self, 'arg%d' % i, arg)
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allargs = list(self.args)
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allshortargs = list(self.args)
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if self.varargs:
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allargs.append('*' + self.varargs)
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allshortargs.append('*' + self.varargs)
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elif self.kwonlyargs:
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allargs.append('*') # single star syntax
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for a in self.kwonlyargs:
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allargs.append('%s=None' % a)
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allshortargs.append('%s=%s' % (a, a))
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if self.varkw:
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allargs.append('**' + self.varkw)
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allshortargs.append('**' + self.varkw)
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self.signature = ', '.join(allargs)
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self.shortsignature = ', '.join(allshortargs)
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self.dict = func.__dict__.copy()
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# func=None happens when decorating a caller
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if name:
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self.name = name
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if signature is not None:
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self.signature = signature
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if defaults:
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self.defaults = defaults
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if doc:
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self.doc = doc
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if module:
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self.module = module
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if funcdict:
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self.dict = funcdict
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# check existence required attributes
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assert hasattr(self, 'name')
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if not hasattr(self, 'signature'):
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raise TypeError('You are decorating a non function: %s' % func)
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def update(self, func, **kw):
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"Update the signature of func with the data in self"
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func.__name__ = self.name
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func.__doc__ = getattr(self, 'doc', None)
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func.__dict__ = getattr(self, 'dict', {})
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func.__defaults__ = self.defaults
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func.__kwdefaults__ = self.kwonlydefaults or None
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func.__annotations__ = getattr(self, 'annotations', None)
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try:
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frame = sys._getframe(3)
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except AttributeError: # for IronPython and similar implementations
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callermodule = '?'
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else:
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callermodule = frame.f_globals.get('__name__', '?')
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func.__module__ = getattr(self, 'module', callermodule)
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func.__dict__.update(kw)
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def make(self, src_templ, evaldict=None, addsource=False, **attrs):
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"Make a new function from a given template and update the signature"
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src = src_templ % vars(self) # expand name and signature
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evaldict = evaldict or {}
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mo = DEF.search(src)
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if mo is None:
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raise SyntaxError('not a valid function template\n%s' % src)
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name = mo.group(1) # extract the function name
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names = set([name] + [arg.strip(' *') for arg in
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self.shortsignature.split(',')])
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for n in names:
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if n in ('_func_', '_call_'):
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raise NameError('%s is overridden in\n%s' % (n, src))
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if not src.endswith('\n'): # add a newline for old Pythons
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src += '\n'
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# Ensure each generated function has a unique filename for profilers
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# (such as cProfile) that depend on the tuple of (<filename>,
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# <definition line>, <function name>) being unique.
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filename = '<decorator-gen-%d>' % next(self._compile_count)
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try:
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code = compile(src, filename, 'single')
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exec(code, evaldict)
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except Exception:
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print('Error in generated code:', file=sys.stderr)
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print(src, file=sys.stderr)
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raise
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func = evaldict[name]
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if addsource:
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attrs['__source__'] = src
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self.update(func, **attrs)
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return func
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@classmethod
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def create(cls, obj, body, evaldict, defaults=None,
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doc=None, module=None, addsource=True, **attrs):
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"""
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Create a function from the strings name, signature and body.
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evaldict is the evaluation dictionary. If addsource is true an
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attribute __source__ is added to the result. The attributes attrs
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are added, if any.
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"""
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if isinstance(obj, str): # "name(signature)"
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name, rest = obj.strip().split('(', 1)
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signature = rest[:-1] # strip a right parens
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func = None
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else: # a function
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name = None
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signature = None
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func = obj
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self = cls(func, name, signature, defaults, doc, module)
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ibody = '\n'.join(' ' + line for line in body.splitlines())
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caller = evaldict.get('_call_') # when called from `decorate`
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if caller and iscoroutinefunction(caller):
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body = ('async def %(name)s(%(signature)s):\n' + ibody).replace(
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'return', 'return await')
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else:
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body = 'def %(name)s(%(signature)s):\n' + ibody
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return self.make(body, evaldict, addsource, **attrs)
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def decorate(func, caller, extras=()):
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"""
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decorate(func, caller) decorates a function using a caller.
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If the caller is a generator function, the resulting function
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will be a generator function.
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"""
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evaldict = dict(_call_=caller, _func_=func)
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es = ''
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for i, extra in enumerate(extras):
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ex = '_e%d_' % i
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evaldict[ex] = extra
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es += ex + ', '
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if '3.5' <= sys.version < '3.6':
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# with Python 3.5 isgeneratorfunction returns True for all coroutines
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# however we know that it is NOT possible to have a generator
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# coroutine in python 3.5: PEP525 was not there yet
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generatorcaller = isgeneratorfunction(
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caller) and not iscoroutinefunction(caller)
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else:
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generatorcaller = isgeneratorfunction(caller)
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if generatorcaller:
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fun = FunctionMaker.create(
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func, "for res in _call_(_func_, %s%%(shortsignature)s):\n"
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" yield res" % es, evaldict, __wrapped__=func)
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else:
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fun = FunctionMaker.create(
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func, "return _call_(_func_, %s%%(shortsignature)s)" % es,
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evaldict, __wrapped__=func)
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if hasattr(func, '__qualname__'):
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fun.__qualname__ = func.__qualname__
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return fun
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def decorator(caller, _func=None):
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"""decorator(caller) converts a caller function into a decorator"""
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if _func is not None: # return a decorated function
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# this is obsolete behavior; you should use decorate instead
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return decorate(_func, caller)
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# else return a decorator function
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defaultargs, defaults = '', ()
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if inspect.isclass(caller):
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name = caller.__name__.lower()
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doc = 'decorator(%s) converts functions/generators into ' \
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'factories of %s objects' % (caller.__name__, caller.__name__)
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elif inspect.isfunction(caller):
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if caller.__name__ == '<lambda>':
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name = '_lambda_'
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else:
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name = caller.__name__
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doc = caller.__doc__
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nargs = caller.__code__.co_argcount
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ndefs = len(caller.__defaults__ or ())
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defaultargs = ', '.join(caller.__code__.co_varnames[nargs-ndefs:nargs])
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if defaultargs:
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defaultargs += ','
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defaults = caller.__defaults__
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else: # assume caller is an object with a __call__ method
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name = caller.__class__.__name__.lower()
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doc = caller.__call__.__doc__
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evaldict = dict(_call=caller, _decorate_=decorate)
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dec = FunctionMaker.create(
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'%s(func, %s)' % (name, defaultargs),
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'if func is None: return lambda func: _decorate_(func, _call, (%s))\n'
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'return _decorate_(func, _call, (%s))' % (defaultargs, defaultargs),
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evaldict, doc=doc, module=caller.__module__, __wrapped__=caller)
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if defaults:
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dec.__defaults__ = (None,) + defaults
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return dec
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# ####################### contextmanager ####################### #
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try: # Python >= 3.2
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from contextlib import _GeneratorContextManager
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except ImportError: # Python >= 2.5
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from contextlib import GeneratorContextManager as _GeneratorContextManager
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class ContextManager(_GeneratorContextManager):
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def __call__(self, func):
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"""Context manager decorator"""
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return FunctionMaker.create(
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func, "with _self_: return _func_(%(shortsignature)s)",
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dict(_self_=self, _func_=func), __wrapped__=func)
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init = getfullargspec(_GeneratorContextManager.__init__)
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n_args = len(init.args)
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if n_args == 2 and not init.varargs: # (self, genobj) Python 2.7
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def __init__(self, g, *a, **k):
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return _GeneratorContextManager.__init__(self, g(*a, **k))
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ContextManager.__init__ = __init__
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elif n_args == 2 and init.varargs: # (self, gen, *a, **k) Python 3.4
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pass
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elif n_args == 4: # (self, gen, args, kwds) Python 3.5
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def __init__(self, g, *a, **k):
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return _GeneratorContextManager.__init__(self, g, a, k)
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ContextManager.__init__ = __init__
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_contextmanager = decorator(ContextManager)
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def contextmanager(func):
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# Enable Pylint config: contextmanager-decorators=decorator.contextmanager
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return _contextmanager(func)
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# ############################ dispatch_on ############################ #
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def append(a, vancestors):
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"""
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Append ``a`` to the list of the virtual ancestors, unless it is already
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included.
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"""
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add = True
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for j, va in enumerate(vancestors):
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if issubclass(va, a):
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add = False
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break
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if issubclass(a, va):
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vancestors[j] = a
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add = False
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if add:
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vancestors.append(a)
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# inspired from simplegeneric by P.J. Eby and functools.singledispatch
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def dispatch_on(*dispatch_args):
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"""
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Factory of decorators turning a function into a generic function
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dispatching on the given arguments.
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"""
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assert dispatch_args, 'No dispatch args passed'
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dispatch_str = '(%s,)' % ', '.join(dispatch_args)
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def check(arguments, wrong=operator.ne, msg=''):
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"""Make sure one passes the expected number of arguments"""
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if wrong(len(arguments), len(dispatch_args)):
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raise TypeError('Expected %d arguments, got %d%s' %
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(len(dispatch_args), len(arguments), msg))
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def gen_func_dec(func):
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"""Decorator turning a function into a generic function"""
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# first check the dispatch arguments
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argset = set(getfullargspec(func).args)
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if not set(dispatch_args) <= argset:
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raise NameError('Unknown dispatch arguments %s' % dispatch_str)
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typemap = {}
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def vancestors(*types):
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"""
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Get a list of sets of virtual ancestors for the given types
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"""
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check(types)
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ras = [[] for _ in range(len(dispatch_args))]
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for types_ in typemap:
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for t, type_, ra in zip(types, types_, ras):
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if issubclass(t, type_) and type_ not in t.mro():
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append(type_, ra)
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return [set(ra) for ra in ras]
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def ancestors(*types):
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"""
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Get a list of virtual MROs, one for each type
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"""
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check(types)
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lists = []
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for t, vas in zip(types, vancestors(*types)):
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n_vas = len(vas)
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if n_vas > 1:
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raise RuntimeError(
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'Ambiguous dispatch for %s: %s' % (t, vas))
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elif n_vas == 1:
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va, = vas
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mro = type('t', (t, va), {}).mro()[1:]
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else:
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mro = t.mro()
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lists.append(mro[:-1]) # discard t and object
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return lists
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def register(*types):
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"""
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Decorator to register an implementation for the given types
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"""
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check(types)
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def dec(f):
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check(getfullargspec(f).args, operator.lt, ' in ' + f.__name__)
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typemap[types] = f
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return f
|
||
|
return dec
|
||
|
|
||
|
def dispatch_info(*types):
|
||
|
"""
|
||
|
An utility to introspect the dispatch algorithm
|
||
|
"""
|
||
|
check(types)
|
||
|
lst = []
|
||
|
for anc in itertools.product(*ancestors(*types)):
|
||
|
lst.append(tuple(a.__name__ for a in anc))
|
||
|
return lst
|
||
|
|
||
|
def _dispatch(dispatch_args, *args, **kw):
|
||
|
types = tuple(type(arg) for arg in dispatch_args)
|
||
|
try: # fast path
|
||
|
f = typemap[types]
|
||
|
except KeyError:
|
||
|
pass
|
||
|
else:
|
||
|
return f(*args, **kw)
|
||
|
combinations = itertools.product(*ancestors(*types))
|
||
|
next(combinations) # the first one has been already tried
|
||
|
for types_ in combinations:
|
||
|
f = typemap.get(types_)
|
||
|
if f is not None:
|
||
|
return f(*args, **kw)
|
||
|
|
||
|
# else call the default implementation
|
||
|
return func(*args, **kw)
|
||
|
|
||
|
return FunctionMaker.create(
|
||
|
func, 'return _f_(%s, %%(shortsignature)s)' % dispatch_str,
|
||
|
dict(_f_=_dispatch), register=register, default=func,
|
||
|
typemap=typemap, vancestors=vancestors, ancestors=ancestors,
|
||
|
dispatch_info=dispatch_info, __wrapped__=func)
|
||
|
|
||
|
gen_func_dec.__name__ = 'dispatch_on' + dispatch_str
|
||
|
return gen_func_dec
|