"""
.. currentmodule:: neet.boolean.randomnet
.. testsetup:: randomnet
import numpy as np
from neet.boolean.examples import *
from neet.boolean.randomnet import *
def neighbors_in(net):
return [net.neighbors_in(node) for node in range(net.size)]
def neighbors_out(net):
return [net.neighbors_out(node) for node in range(net.size)]
def in_degree(net):
return [len(net.neighbors_in(node)) for node in range(net.size)]
def out_degree(net):
return [len(net.neighbors_out(node)) for node in range(net.size)]
def mean_degree(net):
return np.mean(out_degree(net))
Random networks
===============
"""
import random
import numpy as np
from neet.sensitivity import canalizing_nodes
from .logicnetwork import LogicNetwork
[docs]def random_logic(logic_net, p=0.5, connections='fixed-structure',
fix_external=False, make_irreducible=False,
fix_canalizing=False):
"""
Return a ``LogicNetwork`` from an input ``LogicNetwork`` with a random
logic table.
``connections`` decides how a node in the random network is connected to
other nodes:
``'fixed-structure'``
the random network has the same connections as the input network.
``'fixed-in-degree'``
the number of connections to a node is the same as the input
network, but the connections are randomly selected.
``'fixed-mean-degree'``
the total number of edges is conserved, but edges are placed
randomly between nodes.
``'free'``
only the number of nodes is conserved, with the number of
connections to a node chosen uniformly between 1 and the total
number of nodes.
``p`` is the probability of a state of the connected nodes being present in
the activation table. It is also equavolent to the probability of any node
being activated. If ``p`` is a single number, it applies to all nodes.
Otherwise ``p`` must be a sequence of numbers that match in size with the
input network.
.. doctest:: randomnet
>>> net = random_logic(myeloid, connections='fixed-structure')
>>> neighbors_in(net) == neighbors_in(myeloid)
True
>>> neighbors_out(net) == neighbors_out(myeloid)
True
.. doctest:: randomnet
>>> net = random_logic(myeloid, connections='fixed-in-degree')
>>> in_degree(net) == in_degree(myeloid)
True
>>> out_degree(net) == out_degree(myeloid)
False
.. doctest:: randomnet
>>> net = random_logic(myeloid, connections='fixed-mean-degree')
>>> mean_degree(net) == mean_degree(myeloid)
True
:param logic_net: a :class:`neet.boolean.LogicNetwork`
:param p: probability that a state is present in the activation table
:type p: number or sequence
:param connections: ``'fixed-structure'``, ``'fixed-in-degree'``,
``'fixed-mean-degree'``, or ``'free'``
:type connections: str
:returns: a random :class:`neet.boolean.LogicNetwork`
"""
if not isinstance(logic_net, LogicNetwork):
raise ValueError('object must be a LogicNetwork')
if isinstance(p, (int, float)):
ps = [p] * logic_net.size
elif len(p) != logic_net.size:
raise ValueError("p's length must match with network size")
else:
ps = p
random_styles = {'fixed-structure': _random_logic_fixed_connections,
'fixed-in-degree': _random_logic_shuffled_connections,
'fixed-mean-degree': _random_logic_fixed_num_edges,
'free': _random_logic_free_connections}
try:
return random_styles[connections](logic_net, ps, fix_external,
make_irreducible, fix_canalizing)
except KeyError:
msg = "connections must be 'fixed', 'fixed-in-degree'," \
"'fixed-mean-degree', or 'free'"
raise ValueError(msg)
def random_binary_states(k, p):
"""
Return a set of binary states. Each state has length `k` and the number
of states is `k * p` (or chosen to produce `k * p` on average if `n * p`
is not an integer).
"""
integer, decimal = divmod(2**k * p, 1)
num_states = int(integer + np.random.choice(2, p=[1 - decimal, decimal]))
state_idxs = np.random.choice(2 ** k, num_states, replace=False)
return set('{0:0{1}b}'.format(idx, k) for idx in state_idxs)
def random_canalizing_binary_states(k, p):
"""
Return a set of binary states that, when considered as a set of
activating conditions, represents a canalizing function.
Designed to sample each possible canalized function with equal
probability.
Each state has length `k` and the number of states is set in the
same way as `random_binary_states`.
"""
integer, decimal = divmod(2**k * p, 1)
num_states = int(integer + np.random.choice(2, p=[1 - decimal, decimal]))
# calculate values specifying which input is canalizing and how
canalizing_input = np.random.choice(k)
canalizing_value = np.random.choice(2)
if num_states > 2**(k - 1):
canalized_value = 1
elif num_states < 2**(k - 1):
canalized_value = 0
elif num_states == 2**(k - 1):
canalized_value = np.random.choice(2)
fixed_states = _all_states_with_one_node_fixed(
k, canalizing_input, canalizing_value)
other_states = np.lib.arraysetops.setxor1d(np.arange(2**k),
fixed_states,
assume_unique=True)
if canalized_value == 1:
# include all fixed_states as activating conditions
state_idxs = np.random.choice(other_states,
num_states - len(fixed_states),
replace=False)
state_idxs = np.concatenate((state_idxs, np.array(fixed_states)))
elif canalized_value == 0:
# include none of fixed_states as activating conditions
state_idxs = np.random.choice(other_states, num_states, replace=False)
return set('{0:0{1}b}'.format(idx, k) for idx in state_idxs)
def _all_states_with_one_node_fixed(k, fixed_index, fixed_value, max_k=20):
"""
(Should have length 2**(k-1).)
"""
if k > max_k:
raise Exception("k > max_k")
# there may be a more efficient way to do this...
return [idx for idx in range(2**k)
if '{0:0{1}b}'.format(idx, k)[fixed_index] == str(fixed_value)]
def _external_nodes(logic_net):
externals = set()
for idx, row in enumerate(logic_net.table):
if row[0] == (idx, ) and row[1] == {'1'}:
externals.add(idx)
return externals
# stolen from grn-survey.generate_variants
def _fake_connections(net):
fakes = []
for idx in range(net.size):
for neighbor_in in net.neighbors_in(idx):
if not net.is_dependent(idx, neighbor_in):
fakes.append((idx, neighbor_in))
return fakes
def _logic_table_row_is_irreducible(row, i, size):
table = [((), set()) for j in range(size)]
table[i] = row
net = LogicNetwork(table)
return len(_fake_connections(net)) == 0
def _logic_table_row_is_canalizing(row, i, size):
table = [((), set()) for j in range(size)]
table[i] = row
net = LogicNetwork(table)
return i in canalizing_nodes(net)
def _random_logic_fixed_connections(logic_net, ps, fix_external=False,
make_irreducible=False,
fix_canalizing=False,
give_up_number=1000):
"""
Return a `LogicNetwork` from an input `LogicNetwork` with a random logic
table.
Connections in the returned network are the same as those of the input.
:param logic_net: a :class:LogicNetwork
:param ps: probability that a state is present in the activation table
:returns: a random :class:LogicNetwork
"""
if not isinstance(logic_net, LogicNetwork):
raise ValueError('object must be a LogicNetwork')
externals = _external_nodes(logic_net)
new_table = []
for i, row in enumerate(logic_net.table):
indices = row[0]
if i in externals:
conditions = row[1]
else:
if fix_canalizing:
original_canalizing = _logic_table_row_is_canalizing(
row, i, logic_net.size)
keep_trying = True
number_tried = 0
while keep_trying and (number_tried < give_up_number):
if fix_canalizing and original_canalizing:
conditions = random_canalizing_binary_states(
len(indices), ps[i])
else:
conditions = random_binary_states(len(indices), ps[i])
number_tried += 1
keep_trying = False
if make_irreducible:
node_irreducible = _logic_table_row_is_irreducible(
(indices, conditions), i, logic_net.size)
keep_trying = not node_irreducible
if (not keep_trying) and fix_canalizing:
node_canalizing = _logic_table_row_is_canalizing(
(indices, conditions), i, logic_net.size)
keep_trying = not (node_canalizing == original_canalizing)
if number_tried >= give_up_number:
msg = "No function out of " + str(give_up_number) + " tried" \
" satisfied constraints"
raise Exception(msg)
new_table.append((indices, conditions))
return LogicNetwork(new_table, logic_net.names)
def _random_logic_shuffled_connections(logic_net, ps, fix_external=False,
make_irreducible=False,
fix_canalizing=False,
give_up_number=1000):
"""
Return a `LogicNetwork` from an input `LogicNetwork` with a random logic
table.
The number of connections to a node is the same as the input network, but
the connections are randomly selected.
:param logic_net: a :class:LogicNetwork
:param p: probability that a state is present in the activation table
:returns: a random :class:LogicNetwork
"""
if not isinstance(logic_net, LogicNetwork):
raise ValueError('object must be a LogicNetwork')
externals = _external_nodes(logic_net) if fix_external else set()
new_table = []
for i, row in enumerate(logic_net.table):
if i in externals:
indices, conditions = row
else:
if fix_canalizing:
original_canalizing = _logic_table_row_is_canalizing(
row, i, logic_net.size)
keep_trying = True
number_tried = 0
while keep_trying and (number_tried < give_up_number):
n_indices = len(row[0])
indices = tuple(sorted(random.sample(
range(logic_net.size), k=n_indices)))
if fix_canalizing and original_canalizing:
conditions = random_canalizing_binary_states(
n_indices, ps[i])
else:
conditions = random_binary_states(n_indices, ps[i])
number_tried += 1
keep_trying = False
if make_irreducible:
node_irreducible = _logic_table_row_is_irreducible(
(indices, conditions), i, logic_net.size)
keep_trying = not node_irreducible
if (not keep_trying) and fix_canalizing:
node_canalizing = _logic_table_row_is_canalizing(
(indices, conditions), i, logic_net.size)
keep_trying = not (node_canalizing == original_canalizing)
if number_tried >= give_up_number:
msg = "No function out of " + str(give_up_number) + \
" tried satisfied constraints"
raise Exception(msg)
new_table.append((indices, conditions))
return LogicNetwork(new_table, logic_net.names)
def _random_logic_free_connections(logic_net, ps):
"""
Return a `LogicNetwork` from an input `LogicNetwork` with a random logic
table.
All possible connections within the network are considered in the random
process.
:param logic_net: a :class:LogicNetwork
:param p: probability that a state is present in the activation table
:returns: a random :class:LogicNetwork
"""
if not isinstance(logic_net, LogicNetwork):
raise ValueError('object must be a LogicNetwork')
new_table = []
for i in range(logic_net.size):
n_indices = random.randint(1, logic_net.size)
indices = tuple(sorted(random.sample(
range(logic_net.size), k=n_indices)))
conditions = random_binary_states(n_indices, ps[i])
new_table.append((indices, conditions))
return LogicNetwork(new_table, logic_net.names)
def _random_logic_fixed_num_edges(logic_net, ps, fix_external=False,
make_irreducible=False,
fix_canalizing=False,
give_up_number=1000):
"""
Returns new network that corresponds to adding a fixed number of
edges between random nodes, with random corresponding boolean rules.
"""
if fix_canalizing:
raise NotImplementedError("fix_canalizing=True not yet implemented")
num_edges = sum(len(logic_net.neighbors_in(i))
for i in range(logic_net.size))
externals = _external_nodes(logic_net) if fix_external else set()
num_edges -= len(externals)
internals = [idx for idx in range(logic_net.size) if idx not in externals]
num_internal_connections = np.zeros(len(internals))
# partition edges among nodes
keep_trying = True
number_tried = 0
while keep_trying and (number_tried < give_up_number):
rng = range(len(internals) * logic_net.size)
options = [i // logic_net.size for i in rng]
sample = np.random.choice(options, num_edges - len(internals),
replace=False)
idxs, counts = np.unique(sample, return_counts=True)
num_internal_connections[idxs] = counts
num_internal_connections += 1
number_tried += 1
# we need to check that there is no node that will want
# more connections than there are nodes
if max(num_internal_connections) <= logic_net.size:
keep_trying = False
if number_tried >= give_up_number:
raise Exception("No partition out of " +
str(give_up_number) + " tried satisfied constraints")
new_table = [()] * logic_net.size
for internal, num in zip(internals, num_internal_connections):
keep_trying = True
number_tried = 0
while keep_trying and (number_tried < give_up_number):
in_indices = tuple(np.random.choice(
logic_net.size, int(num), replace=False))
conditions = random_binary_states(len(in_indices), ps[internal])
new_table[internal] = (in_indices, conditions)
number_tried += 1
if make_irreducible:
node_irreducible = _logic_table_row_is_irreducible(
(in_indices, conditions), internal, logic_net.size)
keep_trying = not node_irreducible
else:
keep_trying = False
if number_tried >= give_up_number:
raise Exception("No function out of " +
str(give_up_number) +
" tried satisfied constraints")
for external in externals:
new_table[external] = logic_net.table[external]
return LogicNetwork(new_table, logic_net.names)