Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
415 lines
12 KiB
415 lines
12 KiB
"""
|
|
Miscellaneous Helpers for NetworkX.
|
|
|
|
These are not imported into the base networkx namespace but
|
|
can be accessed, for example, as
|
|
|
|
>>> import networkx
|
|
>>> networkx.utils.is_list_of_ints([1, 2, 3])
|
|
True
|
|
>>> networkx.utils.is_list_of_ints([1, 2, "spam"])
|
|
False
|
|
"""
|
|
|
|
from collections import defaultdict
|
|
from collections import deque
|
|
import warnings
|
|
import sys
|
|
import uuid
|
|
from itertools import tee, chain
|
|
import networkx as nx
|
|
|
|
|
|
# some cookbook stuff
|
|
# used in deciding whether something is a bunch of nodes, edges, etc.
|
|
# see G.add_nodes and others in Graph Class in networkx/base.py
|
|
|
|
|
|
def is_string_like(obj): # from John Hunter, types-free version
|
|
"""Check if obj is string."""
|
|
msg = (
|
|
"is_string_like is deprecated and will be removed in 3.0."
|
|
"Use isinstance(obj, str) instead."
|
|
)
|
|
warnings.warn(msg, DeprecationWarning)
|
|
return isinstance(obj, str)
|
|
|
|
|
|
def iterable(obj):
|
|
""" Return True if obj is iterable with a well-defined len()."""
|
|
if hasattr(obj, "__iter__"):
|
|
return True
|
|
try:
|
|
len(obj)
|
|
except:
|
|
return False
|
|
return True
|
|
|
|
|
|
def empty_generator():
|
|
""" Return a generator with no members """
|
|
yield from ()
|
|
|
|
|
|
def flatten(obj, result=None):
|
|
""" Return flattened version of (possibly nested) iterable object. """
|
|
if not iterable(obj) or is_string_like(obj):
|
|
return obj
|
|
if result is None:
|
|
result = []
|
|
for item in obj:
|
|
if not iterable(item) or is_string_like(item):
|
|
result.append(item)
|
|
else:
|
|
flatten(item, result)
|
|
return obj.__class__(result)
|
|
|
|
|
|
def make_list_of_ints(sequence):
|
|
"""Return list of ints from sequence of integral numbers.
|
|
|
|
All elements of the sequence must satisfy int(element) == element
|
|
or a ValueError is raised. Sequence is iterated through once.
|
|
|
|
If sequence is a list, the non-int values are replaced with ints.
|
|
So, no new list is created
|
|
"""
|
|
if not isinstance(sequence, list):
|
|
result = []
|
|
for i in sequence:
|
|
errmsg = f"sequence is not all integers: {i}"
|
|
try:
|
|
ii = int(i)
|
|
except ValueError:
|
|
raise nx.NetworkXError(errmsg) from None
|
|
if ii != i:
|
|
raise nx.NetworkXError(errmsg)
|
|
result.append(ii)
|
|
return result
|
|
# original sequence is a list... in-place conversion to ints
|
|
for indx, i in enumerate(sequence):
|
|
errmsg = f"sequence is not all integers: {i}"
|
|
if isinstance(i, int):
|
|
continue
|
|
try:
|
|
ii = int(i)
|
|
except ValueError:
|
|
raise nx.NetworkXError(errmsg) from None
|
|
if ii != i:
|
|
raise nx.NetworkXError(errmsg)
|
|
sequence[indx] = ii
|
|
return sequence
|
|
|
|
|
|
def is_list_of_ints(intlist):
|
|
""" Return True if list is a list of ints. """
|
|
if not isinstance(intlist, list):
|
|
return False
|
|
for i in intlist:
|
|
if not isinstance(i, int):
|
|
return False
|
|
return True
|
|
|
|
|
|
def make_str(x):
|
|
"""Returns the string representation of t."""
|
|
msg = "make_str is deprecated and will be removed in 3.0. Use str instead."
|
|
warnings.warn(msg, DeprecationWarning)
|
|
return str(x)
|
|
|
|
|
|
def generate_unique_node():
|
|
""" Generate a unique node label."""
|
|
return str(uuid.uuid1())
|
|
|
|
|
|
def default_opener(filename):
|
|
"""Opens `filename` using system's default program.
|
|
|
|
Parameters
|
|
----------
|
|
filename : str
|
|
The path of the file to be opened.
|
|
|
|
"""
|
|
from subprocess import call
|
|
|
|
cmds = {
|
|
"darwin": ["open"],
|
|
"linux": ["xdg-open"],
|
|
"linux2": ["xdg-open"],
|
|
"win32": ["cmd.exe", "/C", "start", ""],
|
|
}
|
|
cmd = cmds[sys.platform] + [filename]
|
|
call(cmd)
|
|
|
|
|
|
def dict_to_numpy_array(d, mapping=None):
|
|
"""Convert a dictionary of dictionaries to a numpy array
|
|
with optional mapping."""
|
|
try:
|
|
return dict_to_numpy_array2(d, mapping)
|
|
except (AttributeError, TypeError):
|
|
# AttributeError is when no mapping was provided and v.keys() fails.
|
|
# TypeError is when a mapping was provided and d[k1][k2] fails.
|
|
return dict_to_numpy_array1(d, mapping)
|
|
|
|
|
|
def dict_to_numpy_array2(d, mapping=None):
|
|
"""Convert a dictionary of dictionaries to a 2d numpy array
|
|
with optional mapping.
|
|
|
|
"""
|
|
import numpy
|
|
|
|
if mapping is None:
|
|
s = set(d.keys())
|
|
for k, v in d.items():
|
|
s.update(v.keys())
|
|
mapping = dict(zip(s, range(len(s))))
|
|
n = len(mapping)
|
|
a = numpy.zeros((n, n))
|
|
for k1, i in mapping.items():
|
|
for k2, j in mapping.items():
|
|
try:
|
|
a[i, j] = d[k1][k2]
|
|
except KeyError:
|
|
pass
|
|
return a
|
|
|
|
|
|
def dict_to_numpy_array1(d, mapping=None):
|
|
"""Convert a dictionary of numbers to a 1d numpy array
|
|
with optional mapping.
|
|
|
|
"""
|
|
import numpy
|
|
|
|
if mapping is None:
|
|
s = set(d.keys())
|
|
mapping = dict(zip(s, range(len(s))))
|
|
n = len(mapping)
|
|
a = numpy.zeros(n)
|
|
for k1, i in mapping.items():
|
|
i = mapping[k1]
|
|
a[i] = d[k1]
|
|
return a
|
|
|
|
|
|
def is_iterator(obj):
|
|
"""Returns True if and only if the given object is an iterator
|
|
object.
|
|
|
|
"""
|
|
has_next_attr = hasattr(obj, "__next__") or hasattr(obj, "next")
|
|
return iter(obj) is obj and has_next_attr
|
|
|
|
|
|
def arbitrary_element(iterable):
|
|
"""Returns an arbitrary element of `iterable` without removing it.
|
|
|
|
This is most useful for "peeking" at an arbitrary element of a set,
|
|
but can be used for any list, dictionary, etc., as well::
|
|
|
|
>>> arbitrary_element({3, 2, 1})
|
|
1
|
|
>>> arbitrary_element("hello")
|
|
'h'
|
|
|
|
This function raises a :exc:`ValueError` if `iterable` is an
|
|
iterator (because the current implementation of this function would
|
|
consume an element from the iterator)::
|
|
|
|
>>> iterator = iter([1, 2, 3])
|
|
>>> arbitrary_element(iterator)
|
|
Traceback (most recent call last):
|
|
...
|
|
ValueError: cannot return an arbitrary item from an iterator
|
|
|
|
"""
|
|
if is_iterator(iterable):
|
|
raise ValueError("cannot return an arbitrary item from an iterator")
|
|
# Another possible implementation is ``for x in iterable: return x``.
|
|
return next(iter(iterable))
|
|
|
|
|
|
# Recipe from the itertools documentation.
|
|
def consume(iterator):
|
|
"Consume the iterator entirely."
|
|
# Feed the entire iterator into a zero-length deque.
|
|
deque(iterator, maxlen=0)
|
|
|
|
|
|
# Recipe from the itertools documentation.
|
|
def pairwise(iterable, cyclic=False):
|
|
"s -> (s0, s1), (s1, s2), (s2, s3), ..."
|
|
a, b = tee(iterable)
|
|
first = next(b, None)
|
|
if cyclic is True:
|
|
return zip(a, chain(b, (first,)))
|
|
return zip(a, b)
|
|
|
|
|
|
def groups(many_to_one):
|
|
"""Converts a many-to-one mapping into a one-to-many mapping.
|
|
|
|
`many_to_one` must be a dictionary whose keys and values are all
|
|
:term:`hashable`.
|
|
|
|
The return value is a dictionary mapping values from `many_to_one`
|
|
to sets of keys from `many_to_one` that have that value.
|
|
|
|
For example::
|
|
|
|
>>> from networkx.utils import groups
|
|
>>> many_to_one = {"a": 1, "b": 1, "c": 2, "d": 3, "e": 3}
|
|
>>> groups(many_to_one) # doctest: +SKIP
|
|
{1: {'a', 'b'}, 2: {'c'}, 3: {'d', 'e'}}
|
|
|
|
"""
|
|
one_to_many = defaultdict(set)
|
|
for v, k in many_to_one.items():
|
|
one_to_many[k].add(v)
|
|
return dict(one_to_many)
|
|
|
|
|
|
def to_tuple(x):
|
|
"""Converts lists to tuples.
|
|
|
|
For example::
|
|
|
|
>>> from networkx.utils import to_tuple
|
|
>>> a_list = [1, 2, [1, 4]]
|
|
>>> to_tuple(a_list)
|
|
(1, 2, (1, 4))
|
|
|
|
"""
|
|
if not isinstance(x, (tuple, list)):
|
|
return x
|
|
return tuple(map(to_tuple, x))
|
|
|
|
|
|
def create_random_state(random_state=None):
|
|
"""Returns a numpy.random.RandomState instance depending on input.
|
|
|
|
Parameters
|
|
----------
|
|
random_state : int or RandomState instance or None optional (default=None)
|
|
If int, return a numpy.random.RandomState instance set with seed=int.
|
|
if numpy.random.RandomState instance, return it.
|
|
if None or numpy.random, return the global random number generator used
|
|
by numpy.random.
|
|
"""
|
|
import numpy as np
|
|
|
|
if random_state is None or random_state is np.random:
|
|
return np.random.mtrand._rand
|
|
if isinstance(random_state, np.random.RandomState):
|
|
return random_state
|
|
if isinstance(random_state, int):
|
|
return np.random.RandomState(random_state)
|
|
msg = (
|
|
f"{random_state} cannot be used to generate a numpy.random.RandomState instance"
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
|
|
class PythonRandomInterface:
|
|
try:
|
|
|
|
def __init__(self, rng=None):
|
|
import numpy
|
|
|
|
if rng is None:
|
|
self._rng = numpy.random.mtrand._rand
|
|
self._rng = rng
|
|
|
|
except ImportError:
|
|
msg = "numpy not found, only random.random available."
|
|
warnings.warn(msg, ImportWarning)
|
|
|
|
def random(self):
|
|
return self._rng.random_sample()
|
|
|
|
def uniform(self, a, b):
|
|
return a + (b - a) * self._rng.random_sample()
|
|
|
|
def randrange(self, a, b=None):
|
|
return self._rng.randint(a, b)
|
|
|
|
def choice(self, seq):
|
|
return seq[self._rng.randint(0, len(seq))]
|
|
|
|
def gauss(self, mu, sigma):
|
|
return self._rng.normal(mu, sigma)
|
|
|
|
def shuffle(self, seq):
|
|
return self._rng.shuffle(seq)
|
|
|
|
# Some methods don't match API for numpy RandomState.
|
|
# Commented out versions are not used by NetworkX
|
|
|
|
def sample(self, seq, k):
|
|
return self._rng.choice(list(seq), size=(k,), replace=False)
|
|
|
|
def randint(self, a, b):
|
|
return self._rng.randint(a, b + 1)
|
|
|
|
# exponential as expovariate with 1/argument,
|
|
def expovariate(self, scale):
|
|
return self._rng.exponential(1 / scale)
|
|
|
|
# pareto as paretovariate with 1/argument,
|
|
def paretovariate(self, shape):
|
|
return self._rng.pareto(shape)
|
|
|
|
|
|
# weibull as weibullvariate multiplied by beta,
|
|
# def weibullvariate(self, alpha, beta):
|
|
# return self._rng.weibull(alpha) * beta
|
|
#
|
|
# def triangular(self, low, high, mode):
|
|
# return self._rng.triangular(low, mode, high)
|
|
#
|
|
# def choices(self, seq, weights=None, cum_weights=None, k=1):
|
|
# return self._rng.choice(seq
|
|
|
|
|
|
def create_py_random_state(random_state=None):
|
|
"""Returns a random.Random instance depending on input.
|
|
|
|
Parameters
|
|
----------
|
|
random_state : int or random number generator or None (default=None)
|
|
If int, return a random.Random instance set with seed=int.
|
|
if random.Random instance, return it.
|
|
if None or the `random` package, return the global random number
|
|
generator used by `random`.
|
|
if np.random package, return the global numpy random number
|
|
generator wrapped in a PythonRandomInterface class.
|
|
if np.random.RandomState instance, return it wrapped in
|
|
PythonRandomInterface
|
|
if a PythonRandomInterface instance, return it
|
|
"""
|
|
import random
|
|
|
|
try:
|
|
import numpy as np
|
|
|
|
if random_state is np.random:
|
|
return PythonRandomInterface(np.random.mtrand._rand)
|
|
if isinstance(random_state, np.random.RandomState):
|
|
return PythonRandomInterface(random_state)
|
|
if isinstance(random_state, PythonRandomInterface):
|
|
return random_state
|
|
except ImportError:
|
|
pass
|
|
|
|
if random_state is None or random_state is random:
|
|
return random._inst
|
|
if isinstance(random_state, random.Random):
|
|
return random_state
|
|
if isinstance(random_state, int):
|
|
return random.Random(random_state)
|
|
msg = f"{random_state} cannot be used to generate a random.Random instance"
|
|
raise ValueError(msg)
|
|
|