def find_rep_nodes(nx_g):
"""
Takes a NetworkX graph and finds groups of nodes that are equivalent
up to automorphism
"""
# Creates PyNauty graph and passes it to PyNauty to get orbits
partition = 'member' if nx_g.__dict__.get('power', 1) > 1 else None
pyn_g, node_map = convert_nx_to_pyn(nx_g, partition=partition)
_, _, _, orbits, _ = pyn.autgrp(pyn_g)
# Finds node equivalency dictionary
node_equivs = defaultdict(list)
for node, equiv in enumerate(orbits):
node_equivs[node_map[equiv]].append(node_map[node])
# If multigraph, returns orbits of nodes in first layer
if nx_g.__dict__.get('dimension', 2) > 2:
node_equivs = {
u: [v for l_v, v in equivs if l_v == 0]
for (l_u, u), equivs in list(node_equivs.items()) if l_u == 0
}
else:
node_equivs = {
node: equivs
for node, equivs in node_equivs.items() if nx_g.degree(node) > 1
}
return node_equivs
def bindByOrbits(graph1, graph2):
bondedGraphs = []
orbits1 = autgrp(graph1)[3]
orbits2 = autgrp(graph2)[3]
verticesByOrbit1 = getVerticesByOrbit(orbits1)
verticesByOrbit2 = getVerticesByOrbit(orbits2)
for vertex1 in verticesByOrbit1.values():
for vertex2 in verticesByOrbit2.values():
g1VertexDegree = graph1.getVertexDegree(vertex1)
g2VertexDegree = graph2.getVertexDegree(vertex2)
if g1VertexDegree != g2VertexDegree:
continue
g1 = deepcopy(graph1)
g2 = deepcopy(graph2)
g1.setBindVertex(vertex1)
g2.setBindVertex(vertex2)
bondedGraphs.append(bindGraph(g1, g2))
return bondedGraphs
def apply_to_all_ged_k(G, k, f, statistic=np.mean):
"""
f is the function to apply. Must take (G,aut)
"""
results = []
edges = [(u, v) for u, v in G.edges()]
for edges_to_remove in combinations(edges, k):
G1 = copy.deepcopy(G)
for e in edges_to_remove:
assert (len(e) == 2)
G1.remove_edge(*e)
g = pynauty.Graph(number_of_vertices=G1.number_of_nodes(),
directed=nx.is_directed(G1),
adjacency_dict=get_adjacency_dict(G1))
aut = pynauty.autgrp(g)
results.append(f(G1, aut))
return statistic(results)
def compute_orbits(node_list, A):
'''
WARN:
node_list & A should be a connected component. Don't input multiple connected components.
args:
node_list e.g. ['C', 'N', 'N', 'C', 'C', 'O', 'P', 'O', 'C']
A adj mat n x n np array. 0 edge absent. >0 edge present. Edge colors & edge weights are ignored.
returns:
orbit labels. 1 label per atom.
number of orbit partitions. scalar.
'''
A = adj_mat_2_adj_dict(A)
vertex_coloring = node_list_2_vertex_coloring(node_list)
G = pynauty.Graph(number_of_vertices=len(node_list),
directed=False,
adjacency_dict=A,
vertex_coloring=vertex_coloring)
automorphism_group = pynauty.autgrp(G) #return -> (generators, grpsize1, grpsize2, orbits, numorbits)
# print('automorphism_group', automorphism_group)
n_orbits = automorphism_group[-1] # e.g. 3
orbits = automorphism_group[-2] # e.g. [0 1 2 2 1 0]
return orbits, n_orbits
print(args.files)
for fname in progressbar.progressbar(glob.glob(args.files)):
# TODO: check if the row is already there. If it is, check what fields it already has
name = Path(fname).name
if name in precomputed_names:
# if it's already there, update
s = df.loc[name]
existing_columns = set(s.where(s.notna()).dropna().index)
columns_to_compute = expected_features - existing_columns
elist = s['elist']
G = nx.OrderedGraph()
G.add_edges_from(elist)
g = pynauty.Graph(number_of_vertices=G.number_of_nodes(),
directed=nx.is_directed(G),
adjacency_dict=get_adjacency_dict(G))
aut = pynauty.autgrp(g)
for feature_name in columns_to_compute:
df.at[name, feature_name] = float(
feature_getter_dispatcher(feature_name, G, aut))
else:
# if it's not there yet, build a dictionary with all the stuff
elist, p, res = pickle.load(open(fname, "rb"))
d = {
'name': name,
'p': float(p),
'elist': copy.deepcopy(elist),
'res': copy.deepcopy(res)
}
G = nx.OrderedGraph()
G.add_edges_from(elist)
g = pynauty.Graph(number_of_vertices=G.number_of_nodes(),
def calculate_platform_symmetries(cfg):
"""Calculate the Automorphism Group of a Platform Graph
This task expects three hydra parameters to be available.
**Hydra Parameters**:
* **platform:** the input platform. The task expects a configuration
dict that can be instantiated to a
:class:`~mocasin.common.platform.Platform` object.
* **out:** the output file (extension will be added)
* **mpsym:** a boolean value selecting mpsym as backend (and JSON as output)
Otherwise it outputs plaintext from the python implementation.
"""
platform = hydra.utils.instantiate(cfg["platform"])
log.info("start converting platform to edge graph for automorphisms.")
plat_graph = platform.to_adjacency_dict(include_proc_type_labels=True)
use_mpsym = cfg["mpsym"]
(
adjacency_dict,
num_vertices,
coloring,
nodes_correspondence,
) = aut.to_labeled_edge_graph(plat_graph)
log.info("done converting platform to edge graph for automorphisms.")
# print(nodes_correspondence)
# print(coloring)
# print(len(coloring))
# print(str(edge_graph))
log.info(
"start calculating the automorphism group of the (edge) graph with " +
str(num_vertices) + " nodes using nauty.")
nautygraph = pynauty.Graph(num_vertices, True, adjacency_dict, coloring)
autgrp_edges = pynauty.autgrp(nautygraph)
log.info(
"done calculating the automorphism group of the (edge) graph using nauty."
)
log.info("start coverting automorhpism of edges to nodes.")
autgrp, new_nodes_correspondence = aut.edge_to_node_autgrp(
autgrp_edges[0], nodes_correspondence)
permutations_lists = map(aut.list_to_tuple_permutation, autgrp)
# permutations = map(perm.Permutation,permutations_lists)
# permgrp = perm.PermutationGroup(list(permutations))
# print(permgrp.point_orbit(0))
log.info("done coverting automorhpism of edges to nodes.")
log.info("start writing to file.")
if use_mpsym:
try:
mpsym
except NameError:
log.error(
"Configured for mpsym output but could not load mpsym. Fallback to python implementation"
)
use_mpsym = False
if use_mpsym:
out_filename = str(cfg["out_file"])
mpsym_autgrp = mpsym.ArchGraphAutomorphisms(
[mpsym.Perm(g) for g in autgrp])
json_out = mpsym_autgrp.to_json()
with open(out_filename, "w") as f:
f.write(json_out)
else:
out_filename = cfg["out_file"]
with open(out_filename, "w") as f:
f.write("Platform Graph:")
f.write(str(plat_graph))
# f.write("Edge Group with ~" + str(autgrp_edges[1]) + " * 10^" + str(autgrp_edges[2]) + " elements.\n")
f.write("Symmetry group generators:")
f.write(str(list(permutations_lists)))
f.write("\nCorrespondence:")
f.write(str(new_nodes_correspondence))
log.info("done writing to file.")
adjacency_dict[variable_index].append(intermediate_vertex_index)
num_vertices += 1
# So, in building the adjacency dict, we want to colour constraints in one colour (maybe this changes in the future).
# We then want to partition the variables by their upper bound, lower bound and their value (if any) in the objective function
# Inefficient to itereate through yet again but want it to be as obvious as possible what's going on
# I think that we may also need to consider constraint colourings!
variable_colouring_dict = defaultdict(set)
for n, variable in model.vd.items():
variable_index = graph_indices[variable.name]
obj_coef = obj_var_coefs[
variable.name] if variable.name in obj_var_coefs else 0
variable_colouring_dict[(variable.lb, variable.ub,
obj_coef)].add(variable_index)
# Let's look at some constraint colourings
constraint_colouring_dict = defaultdict(set)
for n, constraint in model.c.items():
constraint_index = graph_indices[constraint.name]
constraint_colouring_dict[(constraint.lb, constraint.ub,
constraint.rhs)].add(constraint_index)
vertex_colourings = []
vertex_colourings.extend(variable_colouring_dict.values())
vertex_colourings.extend(constraint_colouring_dict.values())
# We haven't yet done the vertex_colourings yet but let's just see what we get for now
graph = pynauty.Graph(num_vertices, False, adjacency_dict, vertex_colourings)
aut = pynauty.autgrp(graph)
print(aut[3])
# [[name, Graph, numorbit, grpsize, generators]]
#
# numorbit, grpsize, generators was calculated by dreadnut
#
from data_graphs import graphs
if __name__ == '__main__':
print('Testing pynauty.autgrp()')
print('Python version: ' + sys.version)
print('Starting ...')
passed = 0
failed = 0
for gname, g, numorbit, grpsize, gens in graphs:
print('%-17s ...' % gname, end=' ')
sys.stdout.flush()
generators, order, o2, orbits, orbit_no = autgrp(g)
if generators == gens and orbit_no == numorbit and order == grpsize:
print('OK')
passed += 1
else:
print('failed')
failed +=1
print('... done.')
if failed > 0:
print('passed = %d failed = %d' % (passed, failed))
else:
print('All tests passed.')
def __init__(
self,
graph,
platform,
channels=False,
periodic_boundary_conditions=False,
norm_p=2,
canonical_operations=True,
disable_mpsym=False,
disable_symmetries_test=False,
):
self._topologyGraph = platform.to_adjacency_dict(
include_proc_type_labels=True)
self.graph = graph
self.platform = platform
self._d = len(graph.processes())
init_app_ncs(self, graph)
self._arch_nc_inv = {}
self.channels = channels
self.boundary_conditions = periodic_boundary_conditions
self.p = norm_p
com_mapper = ComFullMapper(graph, platform)
self.list_mapper = ProcPartialMapper(graph, platform, com_mapper)
self.canonical_operations = canonical_operations
n = len(self.platform.processors())
correct = None
if disable_mpsym:
self.sym_library = False
else:
try:
mpsym
except NameError:
self.sym_library = False
else:
self.sym_library = True
if hasattr(platform, "ag"):
self._ag = platform.ag
log.info(
"Symmetries initialized with mpsym: Platform Generator."
)
elif hasattr(platform, "ag_json"):
if exists(platform.ag_json):
self._ag = mpsym.ArchGraphSystem.from_json_file(
platform.ag_json)
if disable_symmetries_test:
log.warning(
"Using symmetries JSON without testing.")
correct = True
else:
try:
correct = checkSymmetries(
platform.to_adjacency_dict(),
self._ag.automorphisms(),
)
except Exception as e:
log.warning(
"An unknown error occurred while reading "
"the embedding JSON file. Did you provide "
"the correct file for the given platform? "
f"({e})")
correct = False
if not correct:
log.warning(
"Symmetries json does not fit platform.")
del self._ag
else:
log.info(
"Symmetries initialized with mpsym: JSON file."
)
else:
log.warning(
"Invalid symmetries JSON path (file does not exist)."
)
if not hasattr(self, "_ag"):
# only calculate this if not already present
log.info("No pre-comupted mpsym symmetry group available."
" Initalizing architecture graph...")
(
adjacency_dict,
num_vertices,
coloring,
self._arch_nc,
) = to_labeled_edge_graph(self._topologyGraph)
nautygraph = pynauty.Graph(num_vertices, True,
adjacency_dict, coloring)
log.info("Architecture graph initialized. Calculating "
"automorphism group using Nauty...")
autgrp_edges = pynauty.autgrp(nautygraph)
autgrp, _ = edge_to_node_autgrp(autgrp_edges[0],
self._arch_nc)
self._ag = mpsym.ArchGraphAutomorphisms(
[mpsym.Perm(g) for g in autgrp])
for node in self._arch_nc:
self._arch_nc_inv[self._arch_nc[node]] = node
# TODO: ensure that nodes_correspondence fits simpleVec
if not self.sym_library:
log.info(
"Using python symmetries: Initalizing architecture graph...")
(
adjacency_dict,
num_vertices,
coloring,
self._arch_nc,
) = to_labeled_edge_graph(self._topologyGraph)
nautygraph = pynauty.Graph(num_vertices, True, adjacency_dict,
coloring)
log.info("Architecture graph initialized. Calculating "
"automorphism group using Nauty...")
autgrp_edges = pynauty.autgrp(nautygraph)
autgrp, _ = edge_to_node_autgrp(autgrp_edges[0], self._arch_nc)
permutations_lists = map(list_to_tuple_permutation, autgrp)
permutations = [
Permutation.fromLists(p, n=n) for p in permutations_lists
]
self._G = PermutationGroup(permutations)
log.info("Initialized automorphism group with internal symmetries")
def find_symmetries(model):
num_vertices = 0
obj_var_coefs = {}
# Going to make this really easy initially by iterating through once initially and building up an index map
graph_indices = {}
variable_names = {}
def add_vertex(name, num):
graph_indices[name] = num
variable_names[num] = name
return num + 1
for n, constraint in model.c.items():
num_vertices = add_vertex(constraint.name, num_vertices)
#graph_indices[constraint.name] = num_vertices
#num_vertices += 1
for n, variable in model.vd.items():
num_vertices = add_vertex(variable.name, num_vertices)
#graph_indices[variable.name] = num_vertices
#num_vertices += 1
# The number of vertices before the inclusion of intermediate vertices
num_model_vertices = num_vertices
# I think that first, we go through the objective function build a map from variable->onj_coeff
standard_objective = generate_standard_repn(model.o)
for i, (variable, coefficient) in enumerate(
zip(standard_objective.linear_vars,
standard_objective.linear_coefs)):
obj_var_coefs[variable.name] = coefficient
# Should probably look up a slightly nicer way to do this?
adjacency_dict = defaultdict(list)
for name, constraint in model.c.items():
# variables that share a coefficient within a constraint can coalesce their intermediate vertices
coalesce_dict = {}
repn = generate_standard_repn(constraint.body)
constraint_index = graph_indices[constraint.name]
for coefficient, variable in zip(repn.linear_coefs, repn.linear_vars):
# So, first we want an intermediate vertex
variable_index = graph_indices[variable.name]
if coefficient == 1:
# For now, if the coefficient is one, we will not use an intermediate vertex
adjacency_dict[constraint_index].append(variable_index)
elif coefficient in coalesce_dict:
intermediate_vertex_index = coalesce_dict[coefficient]
# The intermediate vertex is already connected to the constraint vertex, so here we just need to connect it to this variable
adjacency_dict[variable_index].append(
intermediate_vertex_index)
else:
# We do not yet have an intermediate vertex for this coefficient and vertex, let's make one
intermediate_vertex_index = num_vertices
coalesce_dict[coefficient] = intermediate_vertex_index
adjacency_dict[constraint_index].append(
intermediate_vertex_index)
adjacency_dict[variable_index].append(
intermediate_vertex_index)
num_vertices += 1
# So, in building the adjacency dict, we want to colour constraints in one colour (maybe this changes in the future).
# We then want to partition the variables by their upper bound, lower bound and their value (if any) in the objective function
# Inefficient to itereate through yet again but want it to be as obvious as possible what's going on
# I think that we may also need to consider constraint colourings!
variable_colouring_dict = defaultdict(set)
for n, variable in model.vd.items():
variable_index = graph_indices[variable.name]
obj_coef = obj_var_coefs[
variable.name] if variable.name in obj_var_coefs else 0
variable_colouring_dict[(variable.lb, variable.ub,
obj_coef)].add(variable_index)
# Let's look at some constraint colourings
constraint_colouring_dict = defaultdict(set)
for n, constraint in model.c.items():
constraint_index = graph_indices[constraint.name]
constraint_colouring_dict[(constraint.lb, constraint.ub,
constraint.rhs)].add(constraint_index)
vertex_colourings = []
vertex_colourings.extend(variable_colouring_dict.values())
vertex_colourings.extend(constraint_colouring_dict.values())
# We haven't yet done the vertex_colourings yet but let's just see what we get for now
graph = pynauty.Graph(num_vertices, False, adjacency_dict,
vertex_colourings)
aut = pynauty.autgrp(graph)
symmetry_groups = defaultdict(set)
for index, min_vertex in enumerate(aut[3][:num_model_vertices]):
# I think that we're doing it slightly wrong here, we should use the constraint names here
symmetry_groups[min_vertex].add(variable_names[index])
return set(map(lambda s: frozenset(s), symmetry_groups.values()))
1: [0, 7],
2: [3, 1],
3: [2, 5],
4: [2, 5],
5: [4, 6],
6: [0, 7],
7: [4, 6],
}
if __name__ == "__main__":
print(list_to_tuple_permutation([0, 1, 2, 4, 3, 5])) # [[3, 4]]
print(list_to_tuple_permutation([5, 4, 3, 2, 1,
0])) # [[0, 5], [1, 4], [2, 3]]
print(list_to_tuple_permutation([1, 2, 3, 4, 5,
0])) # [[0, 1, 2, 3, 4, 5]]
square_aut_grp = pynauty.autgrp(pynauty.Graph(8, True, edge_graph_square))
print(str(list(map(list_to_tuple_permutation, square_aut_grp[0]))))
def platform_graph_to_num_dict(platform_graph):
core_to_int = {}
for i, core in enumerate(platform_graph):
core_to_int[core] = i
res = {}
for core in platform_graph:
vals = []
for val in platform_graph[core]:
vals.append(core_to_int[val[0]])
res[core_to_int[core]] = set(vals)
return res
