def random_walk(seed_node, n, folNm): # in graph G
neg_comp = nx_Graph()
neg_comp.add_node(seed_node)
node_num = 1
pres_node = seed_node
extra = 10
while node_num < n + extra:
with open(folNm + "/" + pres_node, 'rb') as f:
neig_list = pickle_load(f)
if not neig_list:
logging_debug("No neighbours")
break
if len(neig_list) != 1:
new_node = rand_choice(list(neig_list.keys()))
else:
new_node = list(neig_list.keys())[0]
wt = neig_list[new_node]
wt_edge = wt['weight']
neg_comp.add_edge(pres_node, new_node, weight=wt_edge)
if len(neg_comp.nodes()) == n:
break
pres_node = new_node
node_num = node_num + 1
# print(pres_node)
return neg_comp
def engine_picker(graph: Graph,
engine: str,
node_mode: bool = False) -> Union[str, Pos]:
"""Generate a position dict."""
if not node_mode:
graph_ = nx_Graph(_reversed_graph(graph).edges)
else:
graph_ = nx_Graph(graph.edges)
if type(engine) != str:
return engine
# TODO: More layout.
# if engine == "spring":
layout: Pos = spring_layout(graph_, scale=100)
inf = float('inf')
x_max = -inf
x_min = inf
y_max = -inf
y_min = inf
for x, y in layout.values():
x = round(float(x), 4)
y = round(float(y), 4)
if x > x_max:
x_max = x
if x < x_min:
x_min = x
if y > y_max:
y_max = y
if y < y_min:
y_min = y
x_cen = (x_max + x_min) / 2
y_cen = (y_max + y_min) / 2
pos: Pos = {
node: (round(float(x), 4) - x_cen, round(float(y), 4) - y_cen)
for node, (x, y) in layout.items()
}
return pos
def starting_edge(folNm, seed_node):
with open(folNm + "/" + seed_node, 'rb') as f:
neig_list = pickle_load(f)
cd = 1
if not neig_list:
cd = 0
return cd, None
imp_neig = max(neig_list.items(), key=lambda elem: elem[1]['weight'])[0]
# Largest weight neighbor - gives the most confident graphs
wt_edge = neig_list[imp_neig]['weight']
g1 = nx_Graph()
g1.add_edge(seed_node, imp_neig, weight=wt_edge)
return cd, g1
def get_max_matchable_edges_networkx(self, ndds=None):
"""Find the set of edges which must be in every maximum matching.
"""
g = nx_Graph()
translate = {}
for edge in self.es:
v1 = edge.source()
v2 = edge.target()
if v2.index() < v1.index():
continue
new_edge = (v1.index(), v2.index())
if self.edge_exists(v2, v1) or edge.donor().is_altruistic():
g.add_node(new_edge[0])
g.add_node(new_edge[1])
g.add_edge(v1.index(), v2.index())
translate[new_edge] = edge
count = len(translate)
for ndd in ndds:
for edge in ndd.edges:
v1 = edge.donor()
v2 = edge.target()
new_edge = (v2.index(), count + v1.index())
g.add_node(new_edge[0])
g.add_node(new_edge[1])
g.add_edge(v2.index(), count + v1.index())
translate[new_edge] = edge
# TODO Add NDD edges to this graph!
largest = max_weight_matching(g)
LOGGER.debug("Largest matching has size %d", len(largest))
edges = []
for v1, v2 in largest.items():
if v1 < v2:
edges.append([v1, v2])
matchable = []
while edges:
v1, v2 = edges.pop()
LOGGER.debug("Testing [%s, %s]", v1, v2)
g.remove_edge(v1, v2)
new_max = max_weight_matching(g)
if len(new_max) < len(largest):
LOGGER.debug("new matching has size %d", len(new_max))
edges = list(filter(lambda x: x[0] in new_max, edges))
matchable.append((v1, v2))
LOGGER.debug("[%s, %s] is matchable", v1, v2)
g.add_edge(v1, v2)
LOGGER.info("Found %s maximally matchable edges" % len(matchable))
return (translate[e] for e in matchable)
def search_metropolis_clique_start(scaler,par_inputs_fn,G_clique): # Picks out of a subset of its neighbors and adds the best node
#print(seed_clique)
with open(par_inputs_fn,'rb') as f:
inputs = pickle_load(f)
with open(inputs['modelfname'],'rb') as f:
model = pickle_load(f)
g1=nx_Graph(G_clique)
# Finding score
(score_prev,comp_bool) = get_score(g1,model,scaler,inputs['model_type'])
# Removing starting points which are not complexes
if comp_bool == 0:
return ([],0)
g1 = met(g1,model,scaler,inputs,score_prev)
return g1
def search_metropolis_clique_start(scaler, par_inputs_fn, G_clique):
# Picks out of a subset of its neighbors and adds the best node
# print(seed_clique)
with open(par_inputs_fn, 'rb') as f:
inputs = pickle_load(f)
with open(inputs['modelfname'], 'rb') as f:
model = pickle_load(f)
g1 = nx_Graph(G_clique)
# Finding score
score_prev, comp_bool = get_score(g1, model, scaler, inputs['model_type'])
# Removing starting points which are not complexes
if score_prev < inputs["classi_thresh"]:
return
a, b = met(g1, model, scaler, inputs, score_prev)
name = " ".join([str(n) for n in g1.nodes()])
with open(folNm_out + "/" + name, 'wb') as f:
pickle_dump((a, b), f)
def starting_edge_update(folNm, seed_node):
with open(folNm + "/" + seed_node, 'rb') as f:
neig_list = pickle_load(f)
for neighh in neig_list:
neig_list[neighh]['graph_neigs'] = [(seed_node,
neig_list[neighh]['weight'])]
cd = 1
if not neig_list:
cd = 0
return cd, None, None
imp_neig = max(neig_list.items(), key=lambda elem: elem[1]['weight'])[0]
# Largest weight neighbor - gives the most confident graphs
wt_edge = neig_list[imp_neig]['weight']
g1 = nx_Graph()
g1.add_edge(seed_node, imp_neig, weight=wt_edge)
neig_list = update_neig_list(neig_list, imp_neig, folNm, g1.nodes())
return cd, g1, neig_list
def search_max_neig(seed_node,scaler,par_inputs_fn):
with open(par_inputs_fn,'rb') as f:
inputs = pickle_load(f)
with open(inputs['modelfname'],'rb') as f:
model = pickle_load(f) # Seed node
logging_debug("Seed node is",seed_node)
folNm = inputs['folNm']
with open(folNm+"/"+seed_node,'rb') as f:
neig_list = pickle_load(f)
folNm_out = inputs['folNm_out']
if not neig_list:
return
imp_neig = max(neig_list) # Largest weight neighbor - gives the most confident graphs
wt = neig_list[imp_neig]
wt_edge = wt['weight']
score_curr = 0
g1=nx_Graph()
g1.add_edge(seed_node,imp_neig,weight=wt_edge)
max_nodes = inputs["max_size"]
while True:
logging_debug("Adding next node")
imp_neigs = dict()
g1_nodes = g1.nodes()
for node in g1_nodes:
# get its max neighbor and weight and store in dict
with open(folNm+"/"+node,'rb') as f:
neig_list = pickle_load(f)
# Remove neighbors already in graph - one small computation to save memory
neig_fin = set(neig_list) - set(g1_nodes)
neig_list = dict([neig for neig in list(neig_list.items()) if neig[0] in neig_fin])
if not neig_list: # Checking if empty
break
imp_neig = max(neig_list)
wt = neig_list[imp_neig]
wt_edge = wt['weight']
imp_neigs[imp_neig] = wt_edge
if not imp_neigs:
logging_debug("No more neighbors to add")
break
node_to_add = max(imp_neigs) # Check again that this is the max
#ADD ALL EDGES OF NEW NODE TO ORIG GRAPH
with open(folNm+"/"+node_to_add,'rb') as f:
its_neig_list = pickle_load(f)
orig_nodes = g1.nodes()
for node in orig_nodes:
if node in its_neig_list:
wt = its_neig_list[node]
wt_edge = wt['weight']
g1.add_edge(node_to_add,node,weight=wt_edge)
if len(g1) > max_nodes:
logging_debug("Max size exceeded")
break
score_prev = score_curr
(score_curr,comp_bool) = get_score(g1,model,scaler,inputs['model_type'])
if comp_bool == 0:
logging_debug("Complex found")
# Remove the node last added
g1.remove_node(node_to_add)
score_curr = score_prev
break
with open(folNm_out+"/"+seed_node,'wb') as f:
pickle_dump((list(g1.nodes()),score_curr),f)
