def test_cycles(self):
K_undir = nx.all_pairs_node_connectivity(self.cycle)
for source in K_undir:
for target, k in K_undir[source].items():
assert_true(k == 2)
K_dir = nx.all_pairs_node_connectivity(self.directed_cycle)
for source in K_dir:
for target, k in K_dir[source].items():
assert_true(k == 1)
def test_paths(self):
K_undir = nx.all_pairs_node_connectivity(self.path)
for source in K_undir:
for target, k in K_undir[source].items():
assert_true(k == 1)
K_dir = nx.all_pairs_node_connectivity(self.directed_path)
for source in K_dir:
for target, k in K_dir[source].items():
if source < target:
assert_true(k == 1)
else:
assert_true(k == 0)
def create_dynamic_density_architecture(n_qubits,
density_prob=0.1,
backend=None,
**kwargs):
# Generate a random graph
graph = nx.gnp_random_graph(n_qubits, density_prob)
# Make sure it is connected
connectivity = nx.all_pairs_node_connectivity(graph)
disconnected = [(v1, v2) for v1, d in connectivity.items()
for v2, score in d.items() if score == 0]
while disconnected != []:
to_add = np.random.choice(len(disconnected))
graph.add_edge(*disconnected[to_add])
connectivity = nx.all_pairs_node_connectivity(graph)
disconnected = [(v1, v2) for v1, d in connectivity.items()
for v2, score in d.items() if score == 0]
# Number the edges
numbered = {}
numbered = {v: i for i, v in enumerate(nx.dfs_postorder_nodes(graph))}
# Make the coupling graph and adjust the numbering
m = nx.to_numpy_array(graph)
perm = [numbered[i] for i in range(n_qubits)]
perm = [perm.index(i) for i in range(n_qubits)]
m = m[perm][:, perm]
# Find the reduce order
spanning_tree = nx.dfs_tree(graph)
#print(*[[numbered[n] for n in edge] for edge in spanning_tree.edges()])
g = Graph(backend=backend)
vertices = g.add_vertices(n_qubits)
g.add_edges([(vertices[v1], vertices[v2])
for v1, v2 in [[numbered[n] for n in edge]
for edge in spanning_tree.edges()]])
arch = Architecture(name="temp",
coupling_graph=g,
backend=backend,
**kwargs)
subgraph = [i for i in range(n_qubits)]
reduce_order = []
while subgraph != []:
non_cutting = arch.non_cutting_vertices(subgraph)
node = max(non_cutting)
reduce_order.append(node)
subgraph.remove(node)
#arch.reduce_order = reduce_order
#reduce_order = []
name = dynamic_size_architecture_name(DENSITY + str(density_prob),
n_qubits)
arch = Architecture(name,
coupling_matrix=m,
reduce_order=reduce_order,
**kwargs)
return arch
def set_connectivity(data):
df = data.copy()
df['DateOfDeparture'] = pd.to_datetime(df['DateOfDeparture'])
df['month'] = df['DateOfDeparture'].dt.week.astype(str)
df['year'] = df['DateOfDeparture'].dt.year.astype(str)
df['arr_dep'] = df[['Departure',
'Arrival']].apply(lambda x: '-'.join(x),
axis=1)
G = nx.Graph()
connectivity = {}
list_dep_arr = zip(df['Departure'], df['Arrival'])
G.add_edges_from(list_dep_arr)
#G.number_of_nodes()
#G.number_of_edges()
connectivity = nx.all_pairs_node_connectivity(G)
for j, jtem in enumerate(connectivity):
if j == 0:
a = [jtem] * len(connectivity[jtem])
d = pd.DataFrame(connectivity[jtem].items())
d = pd.concat([pd.DataFrame(a), d], axis=1)
c = d.copy()
a = [jtem] * len(connectivity[jtem])
d = pd.DataFrame(connectivity[jtem].items())
d = pd.concat([pd.DataFrame(a), d], axis=1)
c = c.append(d)
c['arr_dep'] = c[c.columns[[0]]].apply(lambda x: '-'.join(x),
axis=1)
connectivity_dep_arr = dict(zip(c['arr_dep'], c[1]))
df['connectivity'] = df['arr_dep'].map(connectivity_dep_arr)
return df
def min_connectivity(self, graph):
apnc = nx.all_pairs_node_connectivity(graph)
# start with graph diameter; minimum won't be larger than this
mc = nx.diameter(graph)
for targets in apnc.itervalues():
mc = min(min(targets.itervalues()), mc)
return mc
def min_connectivity(self, graph):
apnc = nx.all_pairs_node_connectivity(graph)
# start with graph diameter; minimum won't be larger than this
mc = nx.diameter(graph)
for targets in apnc.itervalues():
mc = min(min(targets.itervalues()), mc)
return mc
def set_connectivity(data):
df = data.copy()
df['DateOfDeparture'] = pd.to_datetime(df['DateOfDeparture'])
df['month'] = df['DateOfDeparture'].dt.week.astype(str)
df['year'] = df['DateOfDeparture'].dt.year.astype(str)
df['arr_dep'] = df[['Departure','Arrival']].apply(lambda x: '-'.join(x),axis=1)
G = nx.Graph()
connectivity = {}
list_dep_arr = zip(df['Departure'], df['Arrival'])
G.add_edges_from(list_dep_arr)
#G.number_of_nodes()
#G.number_of_edges()
connectivity = nx.all_pairs_node_connectivity(G)
for j, jtem in enumerate(connectivity):
if j==0:
a=[jtem]*len(connectivity[jtem])
d = pd.DataFrame(connectivity[jtem].items())
d = pd.concat([pd.DataFrame(a),d], axis=1)
c = d.copy()
a=[jtem]*len(connectivity[jtem])
d = pd.DataFrame(connectivity[jtem].items())
d = pd.concat([pd.DataFrame(a),d], axis=1)
c = c.append(d)
c['arr_dep'] = c[c.columns[[0]]].apply(lambda x: '-'.join(x),axis=1)
connectivity_dep_arr = dict(zip(c['arr_dep'],c[1]))
df['connectivity'] = df['arr_dep'].map(connectivity_dep_arr)
return df
def test_all_pairs_connectivity_nbunch(self):
G = nx.complete_graph(5)
nbunch = [0, 2, 3]
A = dict.fromkeys(nbunch, dict())
for u, v in itertools.combinations(nbunch, 2):
A[u][v] = nx.node_connectivity(G, u, v)
C = nx.all_pairs_node_connectivity(G, nbunch=nbunch)
assert_equal(sorted((k, sorted(v)) for k, v in A.items()),
sorted((k, sorted(v)) for k, v in C.items()))
def test_all_pairs_connectivity_nbunch_iter(self):
G = nx.complete_graph(5)
nbunch = [0, 2, 3]
A = dict.fromkeys(nbunch, dict())
for u, v in itertools.combinations(nbunch, 2):
A[u][v] = nx.node_connectivity(G, u, v)
C = nx.all_pairs_node_connectivity(G, nbunch=iter(nbunch))
assert_equal(sorted((k, sorted(v)) for k, v in A.items()),
sorted((k, sorted(v)) for k, v in C.items()))
def test_all_pairs_connectivity_nbunch(self):
G = nx.complete_graph(5)
nbunch = [0, 2, 3]
A = {n: {} for n in nbunch}
for u, v in itertools.combinations(nbunch, 2):
A[u][v] = A[v][u] = nx.node_connectivity(G, u, v)
C = nx.all_pairs_node_connectivity(G, nbunch=nbunch)
assert_equal(sorted((k, sorted(v)) for k, v in A.items()),
sorted((k, sorted(v)) for k, v in C.items()))
def test_all_pairs_connectivity_nbunch_iter(self):
G = nx.complete_graph(5)
nbunch = [0, 2, 3]
A = {n: {} for n in nbunch}
for u, v in itertools.combinations(nbunch, 2):
A[u][v] = A[v][u] = nx.node_connectivity(G, u, v)
C = nx.all_pairs_node_connectivity(G, nbunch=iter(nbunch))
assert_equal(sorted((k, sorted(v)) for k, v in A.items()),
sorted((k, sorted(v)) for k, v in C.items()))
def add_shortest_path(rand, graph, min_length=1):
"""Samples a shortest path from A to B and adds attributes to indicate it.
Args:
rand: A random seed for the graph generator. Default= None.
graph: A `nx.Graph`.
min_length: (optional) An `int` minimum number of edges in the shortest
path. Default= 1.
Returns:
The `nx.DiGraph` with the shortest path added.
Raises:
ValueError: All shortest paths are below the minimum length
"""
node_connected = nx.all_pairs_node_connectivity(graph)
# path = nx.all_simple_paths(graph, 1, 4)
paths = []
path_nodes = []
# print
# print("node_connected_list", list(node_connected))
# print(type(node_connected))
i = random.choice(list(node_connected))
source = i
# print(i)
node_connected_pair = {}
node_reachable = []
for x, yy in node_connected.items():
for y, l in yy.items():
if x == i and l > 0:
node_connected_pair[x, y] = l
path = nx.all_simple_paths(graph, x, y)
node_reachable.append(y)
for p in list(path):
paths.extend(list(pairwise(p)))
node_pairs = list(node_connected_pair)
paths = set(paths)
path_nodes = set(path_nodes)
digraph = graph
digraph.add_node(source, source=True)
digraph.add_nodes_from(set_diff(digraph.nodes(), [source]), source=False)
digraph.add_nodes_from(node_reachable, reachable=True)
digraph.add_nodes_from(set_diff(digraph.nodes(), node_reachable),
reachable=False)
digraph.add_nodes_from(set_diff(digraph.nodes(), path_nodes),
solution=False)
digraph.add_nodes_from(path_nodes, solution=True)
digraph.add_edges_from(set_diff(digraph.edges(), paths), solution=False)
digraph.add_edges_from(paths, solution=True)
return digraph
def test_all_pairs_connectivity(self):
G = nx.Graph()
nodes = [0, 1, 2, 3]
G.add_path(nodes)
A = dict.fromkeys(G, dict())
for u, v in itertools.combinations(nodes, 2):
A[u][v] = nx.node_connectivity(G, u, v)
C = nx.all_pairs_node_connectivity(G)
assert_equal(sorted((k, sorted(v)) for k, v in A.items()),
sorted((k, sorted(v)) for k, v in C.items()))
def test_all_pairs_connectivity(self):
G = nx.Graph()
nodes = [0, 1, 2, 3]
nx.add_path(G, nodes)
A = {n: {} for n in G}
for u, v in itertools.combinations(nodes, 2):
A[u][v] = A[v][u] = nx.node_connectivity(G, u, v)
C = nx.all_pairs_node_connectivity(G)
assert (sorted((k, sorted(v)) for k, v in A.items()) == sorted(
(k, sorted(v)) for k, v in C.items()))
def test_all_pairs_connectivity_directed(self):
G = nx.DiGraph()
nodes = [0, 1, 2, 3]
G.add_path(nodes)
A = dict.fromkeys(G, dict())
for u, v in itertools.permutations(nodes, 2):
A[u][v] = nx.node_connectivity(G, u, v)
C = nx.all_pairs_node_connectivity(G)
assert_equal(sorted((k, sorted(v)) for k, v in A.items()),
sorted((k, sorted(v)) for k, v in C.items()))
def test_all_pairs_connectivity(self):
G = nx.Graph()
nodes = [0, 1, 2, 3]
nx.add_path(G, nodes)
A = {n: {} for n in G}
for u, v in itertools.combinations(nodes,2):
A[u][v] = A[v][u] = nx.node_connectivity(G, u, v)
C = nx.all_pairs_node_connectivity(G)
assert_equal(sorted((k, sorted(v)) for k, v in A.items()),
sorted((k, sorted(v)) for k, v in C.items()))
def test_all_pairs_connectivity_directed(self):
G = nx.DiGraph()
nodes = [0, 1, 2, 3]
nx.add_path(G, nodes)
A = {n: {} for n in G}
for u, v in itertools.permutations(nodes, 2):
A[u][v] = nx.node_connectivity(G, u, v)
C = nx.all_pairs_node_connectivity(G)
assert_equal(sorted((k, sorted(v)) for k, v in A.items()),
sorted((k, sorted(v)) for k, v in C.items()))
def node_conn(graph):
# calculating node connectivity
node_connectivity = nx.all_pairs_node_connectivity(graph)
N = graph.number_of_nodes()
node_conn = np.zeros([N, N])
for key1, value1 in node_connectivity.items():
for key2, value2 in value1.items():
node_conn[key1, key2] = value2
return list(np.triu(node_conn).flatten())
def cal_para_order(para_list):
"""
To record the parameter variable's all fixed order.
:param para_list: the url's kinds of url's.
:return: list -> all fixed parameter order.
"""
result_order = list()
order_collection = set()
for order_seg in para_list:
length = len(order_seg)
for i in range(length - 1):
order_collection.add((order_seg[i], order_seg[i + 1]))
print 'G2:', sorted(list(order_collection))
g = nx.DiGraph()
g.add_edges_from(order_collection)
connectivity_dict = dict([])
for source_node, connect_dict in nx.all_pairs_node_connectivity(g).items():
connectivity_dict[source_node] = []
for target_node, hop in connect_dict.items():
if hop > 0:
connectivity_dict[source_node].append(target_node)
print 'Connectivity_Original:', nx.all_pairs_node_connectivity(g)
print 'Connectivity_dict:', connectivity_dict
node_list = g.nodes()
node_length = len(node_list)
for i in range(node_length):
for j in range(i + 1, node_length):
node_1 = node_list[i]
node_2 = node_list[j]
connect_1_2 = node_2 in connectivity_dict[node_1]
connect_2_1 = node_1 in connectivity_dict[node_2]
if connect_1_2 and not connect_2_1:
result_order.append((node_1, node_2))
elif connect_2_1 and not connect_1_2:
result_order.append((node_2, node_1))
else:
continue
return result_order
def approximation(self):
result = {}
result['all_pairs_node_connectivity'] = nx.all_pairs_node_connectivity(
self.graph)
result['node_connectivity'] = nx.node_connectivity(self.graph)
if self.directed == 'undirected':
result['k_components'] = nx.k_components(self.graph)
result['average_clustering'] = nx.average_clustering(self.graph)
fname_approx = self.DIR + '/appoximation.json'
with open(fname_approx, "w") as f:
json.dump(result, f, cls=SetEncoder, indent=2)
print(fname_approx)
def compute_node_connectivity(self):
connectivity_dict = nx.all_pairs_node_connectivity(
self.virtual_solution_graph,
self.formulation.graph_container.end_nodes)
min_connectivity = 1e20
total_connectivity = 0
for key, val in connectivity_dict.items():
vals = list(val.values())
total_connectivity += sum(vals)
if min(vals) < min_connectivity:
min_connectivity = min(vals)
# Divide by 2 times the unique (s,t) pairs, since connectivity is computed in both directions
avg_connectivity = total_connectivity / (
2 * self.formulation.graph_container.num_unique_pairs)
return min_connectivity, avg_connectivity
def GetAllPairsNodeConnectivity(G, csvfile):
with open(csvfile, 'w') as file:
data = collections.OrderedDict()
nodes = sorted(nx.nodes(G))
apnc = nx.all_pairs_node_connectivity(G)
# Write Header
for n in nodes:
file.write(',' + str(n))
file.write('\n')
# Create and Write Body
for n in nodes:
s = str(n)
for k in nodes:
s += ','
if n != k:
s += str(apnc[n][k])
file.write(s + '\n')
def main():
pairs_filename = sys.argv[1]
selections_filename = sys.argv[2]
pairs = read_pairs(pairs_filename)
selections = read_selections(selections_filename)
dg = nx.DiGraph()
dg.add_weighted_edges_from(pairs)
connectivity = nx.all_pairs_node_connectivity(dg)
results = OrderedSet()
for k, v in connectivity.items():
if k in selections:
results.add(MAPPING[k])
for k2, v2 in v.items():
if v2:
results.add(MAPPING[k2])
for r in results:
print(r)
def reachable_from(graph, node):
"""
Given an nx graph and a node within this graph,
return all of the nodes in the same connected component as the input node.
"""
import networkx as nx
if isinstance(graph, nx.DiGraph):
conn = nx.all_pairs_node_connectivity(graph)
return [n1 for (n1, dist) in conn[node].iteritems() if dist > 0]
elif isinstance(graph, nx.Graph):
for comp in nx.connected_components(graph):
if node in comp:
return comp
# Shouldn't get here
# since the node should appear in some connected component of the graph
raise Exception("Node {} not in graph".format(node))
def Play_Add_backup(G, N, B, U, C_Val, Path_E, D):
E = nx.all_pairs_node_connectivity(G)
for i in N:
max_u = np.sort(U[i, :])
max_u = max_u[::-1]
# print max_u
m = 0
while (B[i] > 0 and m < NUMBER_OF_NODES and chk_conn(E, i)):
j = U[i, :].tolist().index(max_u[m])
# print j
cost = C_Val[i][j][0] + C_Val[i][j][2]
if U[i, j] > 0 and cost < B[i] and [i, j] not in N_warn:
G.add_edge(i, j)
B[i] = B[i] - cost
m = m + 1
LOG.info('Add Played')
return G, B
def roda_funcoes_grafo(nx_grafo):
# Connectivity (em "Approximations and Heuristics")
print('Connectivity')
print(nx.all_pairs_node_connectivity(nx_grafo))
# Centrality degree (em "Centrality")
print('Centrality degree')
print(nx.degree_centrality(nx_grafo))
# Closeness (em "Centrality")
print('Closeness')
print(nx.closeness_centrality(nx_grafo, distance='weight'))
# (Shortest Path) Betweenness (em "Centrality")
print('Shortest path betweenness')
print(
nx.betweenness_centrality(nx_grafo, normalized=False, weight='weight'))
# Communicability Betweenness
print('Communicability Betweenness')
print(
nx.communicability_betweenness_centrality(nx_grafo.to_undirected(),
normalized=False))
def __cal_variable_order_rule(variable_order_set):
"""
To record the parameter variable's all fixed order.
:param variable_order_set: set((v1, v2, v3), (v3, v4), ...)
:return: set((v1, v2), (v1, v3), ...)
"""
result_order = set()
order_collection = set()
for order_seg in variable_order_set:
for i in range(len(order_seg) - 1):
order_collection.add((order_seg[i], order_seg[i + 1]))
g = nx.DiGraph()
g.add_edges_from(order_collection)
connectivity_dict = dict([])
for source_node, connect_dict in nx.all_pairs_node_connectivity(
g).items():
connectivity_dict[source_node] = set()
for target_node, hop in connect_dict.items():
if hop > 0:
connectivity_dict[source_node].add(target_node)
node_list = g.nodes()
node_count = len(node_list)
for i in range(node_count):
for j in range(i + 1, node_count):
node_1 = node_list[i]
node_2 = node_list[j]
# if node_1 connect to node_2
connect_1_2 = node_2 in connectivity_dict[node_1]
# if node_2 connect to node_1
connect_2_1 = node_1 in connectivity_dict[node_2]
if connect_1_2 and not connect_2_1:
result_order.add((node_1, node_2))
elif connect_2_1 and not connect_1_2:
result_order.add((node_2, node_1))
else:
continue
return result_order
def __cal_variable_order_rule(variable_order_set):
"""
To record the parameter variable's all fixed order.
:param variable_order_set: set((v1, v2, v3), (v3, v4), ...)
:return: set((v1, v2), (v1, v3), ...)
"""
result_order = set()
order_collection = set()
for order_seg in variable_order_set:
for i in range(len(order_seg) - 1):
order_collection.add((order_seg[i], order_seg[i + 1]))
g = nx.DiGraph()
g.add_edges_from(order_collection)
connectivity_dict = dict([])
for source_node, connect_dict in nx.all_pairs_node_connectivity(g).items():
connectivity_dict[source_node] = set()
for target_node, hop in connect_dict.items():
if hop > 0:
connectivity_dict[source_node].add(target_node)
node_list = g.nodes()
node_count = len(node_list)
for i in range(node_count):
for j in range(i + 1, node_count):
node_1 = node_list[i]
node_2 = node_list[j]
# if node_1 connect to node_2
connect_1_2 = node_2 in connectivity_dict[node_1]
# if node_2 connect to node_1
connect_2_1 = node_1 in connectivity_dict[node_2]
if connect_1_2 and not connect_2_1:
result_order.add((node_1, node_2))
elif connect_2_1 and not connect_1_2:
result_order.add((node_2, node_1))
else:
continue
return result_order
def test_all_pairs_connectivity_icosahedral(self):
G = nx.icosahedral_graph()
C = nx.all_pairs_node_connectivity(G)
assert_true(all(5 == C[u][v] for u, v in itertools.combinations(G, 2)))
def test_all_pairs_connectivity_icosahedral(self):
G = nx.icosahedral_graph()
C = nx.all_pairs_node_connectivity(G)
assert_true(all(5 == C[u][v] for u, v in itertools.combinations(G, 2)))
def state_features(self):
f = []
legal_actions = self.get_legal_actions()
if params.use_in_out_matrix:
out_degree = self.G.out_degree(self.I)
for (i, j) in out_degree:
f.extend(
RP_utils.polynomialize(j / params.m,
params.num_polynomial))
in_degree = self.G.in_degree(self.I)
for (i, j) in in_degree:
f.extend(
RP_utils.polynomialize(j / params.m,
params.num_polynomial))
if params.use_in_out_relative_matrix:
out_degree = self.G.out_degree(self.I)
for (i, j) in out_degree:
f.extend(
RP_utils.polynomialize(
RP_utils.safe_div(j, self.E_0.out_degree(i)),
params.num_polynomial))
in_degree = self.G.in_degree(self.I)
for (i, j) in in_degree:
f.extend(
RP_utils.polynomialize(
RP_utils.safe_div(j, self.E_0.in_degree(i)),
params.num_polynomial))
if params.use_total_degree_matrix:
for i in self.I:
i_total = self.G.out_degree(i) + self.G.in_degree(i)
i_e0_total = self.E_0.out_degree(i) + self.E_0.in_degree(i)
f.extend(
RP_utils.polynomialize(
RP_utils.safe_div(i_total, i_e0_total),
params.num_polynomial))
if params.use_in_out_binary_matrix:
out_degree = self.G.out_degree(self.I)
for (i, j) in out_degree:
f.append(2 * int(j > 0) - 1)
in_degree = self.G.in_degree(self.I)
for (i, j) in in_degree:
f.append(2 * int(j > 0) - 1)
if params.use_voting_rules_matrix:
for i in self.plurality_scores:
f.extend(RP_utils.polynomialize(i, params.num_polynomial))
for i in self.borda_scores:
f.extend(RP_utils.polynomialize(i, params.num_polynomial))
for i in self.copeland_scores:
f.extend(RP_utils.polynomialize(i, params.num_polynomial))
for i in self.maximin_scores:
f.extend(RP_utils.polynomialize(i, params.num_polynomial))
if params.use_edge_weight:
f.extend(
RP_utils.polynomialize(
self.E_0_really[legal_actions[0][0]][legal_actions[0][1]]
['weight'] / self.max_edge_weight, params.num_polynomial))
if params.use_vectorized_wmg:
f.extend(self.vectorized_wmg)
if params.use_posmat:
f.extend(self.posmat)
if params.use_tier_adjacency_matrix:
T_matrix = np.zeros((int(params.m), int(params.m)))
for (c1, c2) in legal_actions:
T_matrix[c1, c2] = 1
T_vec = list(T_matrix.flatten())
f.extend(T_vec)
if params.use_connectivity_matrix:
for i in self.I:
for j in self.I:
if i != j:
f.extend(
RP_utils.polynomialize(
local_edge_connectivity(self.G, i, j) /
(params.m - 2), params.num_polynomial)
) # normalized by m-2 since max edges needed to disconnect i and j is all edges but i -> i and i -> j
all_pairs_node_connectivity = nx.all_pairs_node_connectivity(
self.G)
for i in self.I:
for j in self.I:
if i != j:
f.extend(
RP_utils.polynomialize(
all_pairs_node_connectivity[i][j] /
(params.m - 2), params.num_polynomial)
) # normalized by m-2 since max nodes needed to disconnect i and j is all nodes but i and j
# adjacency matrix of current state
if params.use_adjacency_matrix:
adjacency = nx.adjacency_matrix(self.G, nodelist=self.I).todense()
adjacency = np.multiply(adjacency, self.adjacency_0)
adjacency_normalized = np.divide(adjacency, params.n)
f.extend(adjacency_normalized.flatten().tolist()[0])
# K representation
if params.use_K_representation:
K_list = []
for i in self.I:
if i in self.K:
K_list.append(1)
else:
K_list.append(0)
f.extend(K_list)
# node2vec every time
# G_with_weights = nx.DiGraph()
# G_with_weights.add_nodes_from(self.I)
# for (cand1, cand2) in self.G.edges():
# G_with_weights.add_edge(cand1, cand2, weight=self.E_0_really[cand1][cand2]['weight'])
# node2vec_G = node2vec.Graph(G_with_weights, True, self.node2vec_args.p, self.node2vec_args.q)
# node2vec_G.preprocess_transition_probs()
# walks = node2vec_G.simulate_walks(self.node2vec_args.num_walks, self.node2vec_args.walk_length)
# self.node2vec_model = node2vecmain.learn_embeddings(walks, self.node2vec_args)
# node2vec features
# node2vec_u = self.node2vec_model.wv[str(u)]
# node2vec_v = self.node2vec_model.wv[str(v)]
# node2vec_uv = np.append(node2vec_u, node2vec_v)
# node2vec_f = np.append(node2vec_uv, np.array(f))
if params.debug_mode >= 3:
print("features", f)
return Variable(torch.from_numpy(np.array(f)).float())
def state_features(self, G, K, T, profile):
f = []
E_0 = self.profile_to_E0[profile]
adjacency_0 = self.profile_to_adjacency0[profile]
if params.use_in_out_matrix:
out_degree = G.out_degree(self.I)
for (i, j) in out_degree:
f.extend(
RP_utils.polynomialize(
RP_utils.safe_div(j, E_0.out_degree(i)),
params.num_polynomial))
in_degree = G.in_degree(self.I)
for (i, j) in in_degree:
f.extend(
RP_utils.polynomialize(
RP_utils.safe_div(j, E_0.in_degree(i)),
params.num_polynomial))
if params.use_total_degree_matrix:
for i in self.I:
i_total = G.out_degree(i) + G.in_degree(i)
i_e0_total = E_0.out_degree(i) + E_0.in_degree(i)
f.extend(
RP_utils.polynomialize(
RP_utils.safe_div(i_total, i_e0_total),
params.num_polynomial))
if params.use_in_out_binary_matrix:
out_degree = G.out_degree(self.I)
for (i, j) in out_degree:
f.append(2 * int(j > 0) - 1)
in_degree = G.in_degree(self.I)
for (i, j) in in_degree:
f.append(2 * int(j > 0) - 1)
if params.use_voting_rules_matrix:
for i in self.profile_to_plurality[profile]:
f.extend(RP_utils.polynomialize(i, params.num_polynomial))
for i in self.profile_to_borda[profile]:
f.extend(RP_utils.polynomialize(i, params.num_polynomial))
for i in self.profile_to_copeland[profile]:
f.extend(RP_utils.polynomialize(i, params.num_polynomial))
for i in self.profile_to_maximin[profile]:
f.extend(RP_utils.polynomialize(i, params.num_polynomial))
if params.use_edge_weight:
f.extend(
RP_utils.polynomialize(
E_0[T[0][0]][T[0][1]]['weight'] /
self.profile_to_max_edge_weight[profile],
params.num_polynomial))
if params.use_vectorized_wmg:
f.extend(self.profile_to_vectorized_wmg[profile])
if params.use_posmat:
f.extend(self.profile_to_posmat[profile])
if params.use_tier_adjacency_matrix:
T_matrix = np.zeros((int(params.m), int(params.m)))
for (c1, c2) in T:
T_matrix[c1, c2] = 1
T_vec = list(T_matrix.flatten())
f.extend(T_vec)
if params.use_connectivity_matrix:
for i in self.I:
for j in self.I:
if i != j:
f.extend(
RP_utils.polynomialize(
local_edge_connectivity(G, i, j) /
(params.m - 2), params.num_polynomial)
) # normalized by m-2 since max edges needed to disconnect i and j is all edges but i -> i and i -> j
all_pairs_node_connectivity = nx.all_pairs_node_connectivity(G)
for i in self.I:
for j in self.I:
if i != j:
f.extend(
RP_utils.polynomialize(
all_pairs_node_connectivity[i][j] /
(params.m - 2), params.num_polynomial)
) # normalized by m-2 since max nodes needed to disconnect i and j is all nodes but i and j
# adjacency matrix
if params.use_adjacency_matrix:
adjacency = nx.adjacency_matrix(G, nodelist=self.I).todense()
adjacency = np.multiply(adjacency, adjacency_0)
adjacency_normalized = np.divide(adjacency, params.n)
f.extend(adjacency_normalized.flatten().tolist()[0])
# K representation
if params.use_K_representation:
K_list = []
for i in self.I:
if i in K:
K_list.append(1)
else:
K_list.append(0)
f.extend(K_list)
return Variable(torch.from_numpy(np.array(f)).float())
def main():
# Directed Bison Network
bison_file = 'moreno_bison/out.moreno_bison_bison'
bison_graph = nx.DiGraph()
create_network(bison_graph, bison_file, True)
# Undirected Kangaroo Network
kangaroo_file = 'moreno_kangaroo/out.moreno_kangaroo_kangaroo'
kangaroo_graph = nx.Graph()
create_network(kangaroo_graph, kangaroo_file, False)
# Part A: Connected Component Analysis
# Connected Component Analysis of Bison Directed Graph
print("PART A:\n")
print("Bison Directed Graph Connected Component Analysis",
"\nWeakly connected: ", nx.is_weakly_connected(bison_graph),
"\nNumber of Weakly CCs: ",
nx.number_weakly_connected_components(bison_graph),
"\nSize of largest CC: ",
len(max(nx.weakly_connected_components(bison_graph),
key=len)), "\nSize of smallest CC: ",
len(min(nx.weakly_connected_components(bison_graph), key=len)))
# Connected Component Analysis of Kangaroo Undirected Graph
print("\nKangaroo Undirected Graph Connected Component Analysis",
"\nConnected: ", nx.is_connected(kangaroo_graph),
"\nNumber of CCs: ", nx.number_connected_components(kangaroo_graph),
"\nSize of largest CC: ",
len(max(nx.connected_components(kangaroo_graph),
key=len)), "\nSize of smallest CC: ",
len(min(nx.connected_components(kangaroo_graph), key=len)))
# Part B Computing Degrees and finding the Probability distribution
# Creation of an arrayList to store the degree for each node of Bison Network
bison_degrees = []
for node in range(1, 26):
bison_degrees.append(bison_graph.degree(node))
# Computing Mean and Standard Deviation for Directed
x_label = stats(bison_degrees)
# Creating a Histogram to plot the data of the degrees Bison Network
plt.figure(3)
plt.title('Part B: Histogram Directed Bison')
plt.xlabel(x_label)
plt.hist(bison_degrees, bins='auto')
# Creation of an arrayList to store the degree for each node of Kangaroo Network
kangaroo_degrees = []
for node in range(1, 17):
kangaroo_degrees.append(kangaroo_graph.degree(node))
# Computing Mean and Standard Deviation for Undirected
x_label = stats(kangaroo_degrees)
# Creating a Histogram to plot the data of the degrees for Kangaroo Network
plt.figure(4)
plt.title('Part B: Histogram Undirected Kangaroo')
plt.xlabel(x_label)
plt.hist(kangaroo_degrees, bins='auto')
# lt.show()
# Part C Find the Path between 2 abritrary vertices in the largest CC
# Creating two arbritrary nodes making sure they aren't the same number
node1 = random.randrange(1, 27, 1)
node2 = random.randrange(1, 27, 1)
while node1 == node2:
node1 = random.randrange(1, 27, 1)
# I put a cutoff on the list of simple paths for now so I can atleast run something
# cut off is the act of only focusing on the paths <= 5
# This section creates a list of all simple paths and then creates a list with the lengths of these paths
bison_paths = list(nx.all_simple_paths(bison_graph, node1, node2,
cutoff=5))
bison_p_lengths = []
for node in range(0, len(bison_paths) - 1):
bison_p_lengths.append(len(bison_paths[node]))
x_label = stats(bison_p_lengths)
# Creating a histogram for the degrees of the graph
plt.figure(5)
plt.title('Part C: Histogram Directed Bison Paths')
plt.xlabel(x_label)
plt.hist(bison_p_lengths, bins='auto')
# plt.show()
# Creating two arbitrary nodes making sure they aren't the same number
node1 = random.randrange(1, 17, 1)
node2 = random.randrange(1, 17, 1)
while node1 == node2:
node1 = random.randrange(1, 17, 1)
# This section creates a list of all simple paths and then creates a list with the lengths of these paths
kangaroo_paths = list(
nx.all_simple_paths(kangaroo_graph, node1, node2, cutoff=5))
kangaroo_p_lengths = []
for node in range(0, len(kangaroo_paths) - 1):
kangaroo_p_lengths.append(len(kangaroo_paths[node]))
x_label = stats(kangaroo_p_lengths)
# Creating a histogram for the degrees of the graph
plt.figure(6)
plt.title('Part C: Histogram Undirected Kangaroo Paths')
plt.xlabel(x_label)
plt.hist(kangaroo_p_lengths, bins='auto')
# plt.show()
# Part D Find the Simple Circuits between 2 abritrary vertices in the largest CC
# UNABLE TO RUN BISON CIRCUITS ON LAPTOP THERE ARE TO MANY AND I CANNOT CREATE A CUTOFF
# Creates a list of simple cycles and then creates another list of the lengths of the cycles
# bison_circuits = list(nx.simple_cycles(bison_graph))
# bison_c_lengths = []
# for node in range(0,len(bison_circuits)-1):
# bison_c_lengths.append(len(bison_circuits[node]))
#
# x_label = stats(bison_c_lengths)
#
# plt.figure(7)
# plt.title('PART D: Histogram Directed Bison Circuits')
# plt.xlabel(x_label)
# plt.hist(bison_c_lengths, bins = 'auto')
# You can't use the simple cycle function for undirected graphs so I used the basis function.
# Creates a list of simple cycles and then creates another list of the lengths of the cycles
kangaroo_circuits = nx.cycle_basis(kangaroo_graph)
kangaroo_c_lengths = []
for node in range(0, len(kangaroo_circuits) - 1):
kangaroo_c_lengths.append(len(kangaroo_circuits[node]))
x_label = stats(kangaroo_c_lengths)
plt.figure(7)
plt.title('PART D: Histogram Undirected Kangaroo Circuits')
plt.xlabel(x_label)
plt.hist(kangaroo_c_lengths, bins='auto')
# plt.show()
# Part E Check if Eulerian, Find a Eulerian Path
print("\nPART E:")
print("\nDirected Bison Graph")
print("Euelerian: ", nx.is_eulerian(bison_graph))
print("Has a Eulerian Path: ", nx.has_eulerian_path(bison_graph))
print("\nUndirected Kangaroo Graph")
print("Euelerian: ", nx.is_eulerian(kangaroo_graph))
print("Has a Eulerian Path: ", nx.has_eulerian_path(kangaroo_graph))
# Part F: Convert to Matrix.
# I don't know if this covers everything?
bison_matrix = nx.to_numpy_matrix(bison_graph)
plt.matshow(bison_matrix)
# plt.show()
kangaroo_matrix = nx.to_numpy_matrix(kangaroo_graph)
plt.matshow(kangaroo_matrix)
# plt.show()
# Part G: Copy Largest CC comparing it to a copy and a slightly different CC
print("\nPart G:\n")
# copying the largest connected component from the Bison Directed graph
bison_n1 = nx.Graph()
largest_cc_bison = list(
max(nx.weakly_connected_components(bison_graph), key=len))
for i in largest_cc_bison:
bison_n1.add_edge(i, i + 1)
bison_n2 = bison_n1.copy()
# Checking Equivalence between copied graphs
print("Is bison_n1 Equivalent to bison_n2?")
compare(bison_n1, bison_n2)
# Checking Equivalence between copied graphs but one has an extra 10 edges
print("\nIs bison_n1 Equivalent to N3?")
bison_n3 = bison_n2.copy()
add_10_edges(bison_n3, len(bison_n3))
compare(bison_n1, bison_n3)
# Repeat for Kangaroo Undirected Network
kangaroo_n1 = nx.Graph()
largest_cc_kangaroo = list(
max(nx.connected_components(kangaroo_graph), key=len))
for i in largest_cc_kangaroo:
kangaroo_n1.add_edge(i, i + 1)
kangaroo_n2 = kangaroo_n1.copy()
print("\nIs kangaroo_n1 Equivalent to kangaroo_n2?")
compare(kangaroo_n1, kangaroo_n2)
print("\nIs kangaroo_n1 Equivalent to N3?")
kangaroo_n3 = kangaroo_n2.copy()
add_10_edges(kangaroo_n3, len(kangaroo_n3))
compare(kangaroo_n1, kangaroo_n3)
# Part H: Generate Minimum Spanning Tree
print("\nPart H:\n")
# Cannot generate SPanning tree for Directed networks
# Generating a minimum spanning tree for Undirected network
kangaroo_min_tree = nx.minimum_spanning_tree(kangaroo_graph)
print(
"~A Minimum Spanning Tree was created for the Undirected Kangaroo Graph~"
)
tree_or_forest(kangaroo_min_tree)
# Finding two random nodes that are connected
x = 0
y = 0
while (not (kangaroo_min_tree.has_edge(x, y))):
x = random.randrange(1, 17, 1)
y = random.randrange(1, 17, 1)
while x == y:
x = random.randrange(1, 17, 1)
# Removing the found edge
print("\nAn edge from the spanning tree was removed")
kangaroo_min_tree.remove_edge(x, y)
tree_or_forest(kangaroo_min_tree)
# Part I: Dijkstra's Algorithm
bison_pairs = list(nx.all_pairs_node_connectivity(bison_graph))
connected_nodes = []
for i in bison_pairs:
for j in bison_pairs:
if bison_graph.has_edge(i, j + 1):
connected_nodes.append([i, j + 1])
dijkstra_paths = []
length = len(connected_nodes)
for i in range(0, length - 1):
for j in range(0, 1):
dijkstra_paths.append(
int(
nx.dijkstra_path_length(bison_graph, connected_nodes[i][j],
connected_nodes[i][j + 1])))
x_label = stats(dijkstra_paths)
plt.figure()
plt.xlabel(x_label)
plt.title('Directed Bison Dijkstra Path Lengths')
plt.hist(dijkstra_paths)
# plt.show()
#Created a new temporary graph with edges from the connected nodes and weights from the distance list
temp_bison = nx.DiGraph()
for i in range(0, length - 1):
j = 0
temp_bison.add_edge(connected_nodes[i][j],
connected_nodes[i][j + 1],
weight=dijkstra_paths[i])
# I dont really know if this creates a matrix for the weigths this is just what i did in a previous part
bison_distance_matrix = nx.to_numpy_matrix(temp_bison)
plt.matshow(bison_distance_matrix)
plt.show()
# Repeat for Kangaroo Undirected
KangarooPairs = list(nx.all_pairs_node_connectivity(KangarooGraph))
ConnectedNodesK = []
for i in KangarooPairs:
for j in KangarooPairs:
if KangarooGraph.has_edge(i, j + 1):
ConnectedNodesK.append([i, j + 1])
dijkstra_PathsK = []
length = len(ConnectedNodesK)
for i in range(0, length):
dijkstra_PathsK.append(
int(
nx.dijkstra_path_length(KangarooGraph, ConnectedNodesK[i][0],
ConnectedNodesK[i][1])))
xLabel = Stats(dijkstra_PathsK)
plt.figure()
plt.xlabel(xLabel)
plt.title('Undirected Kangaroo Dijkstra Path Lengths')
plt.hist(dijkstra_PathsK)
plt.show()
temp_kangaroo = nx.Graph()
for i in range(0, length - 1):
j = 0
temp_kangaroo.add_edge(ConnectedNodesK[i][j],
ConnectedNodesK[i][j + 1],
weight=dijkstra_PathsK[i])
kangaroo_distance_matrix = nx.to_numpy_matrix(temp_kangaroo)
plt.matshow(kangaroo_distance_matrix)
plt.show()
def reachable(graph):
# graph = nx.DiGraph()
# graph.add_edges_from([(1, 2), (3, 1), (4, 2), (5, 2), (2, 3), (1, 5)])
node_connected = nx.all_pairs_node_connectivity(graph)
path = nx.all_simple_paths(graph, 1, 4)
paths = []
path_nodes = []
# print
print("node_connected_list", list(node_connected))
# print(type(node_connected))
i = random.choice(list(node_connected))
source = i
print(i)
node_connected_pair = {}
node_reachable = []
for x, yy in node_connected.items():
# print("x, y: ", x,yy)
# print(type(y))
for y, l in yy.items():
if x == i and l > 0:
node_connected_pair[x, y] = l
path = nx.all_simple_paths(graph, x, y)
node_reachable.append(y)
# print(path)
# if len(list(path)) > path_max_length:
# longest_path = path
# path_max_length = len(list(path))
for p in list(path):
# print(p)
paths.extend(list(gu.pairwise(p)))
path_nodes.extend(p)
# print(x, y, l)
node_pairs = list(node_connected_pair)
paths = set(paths)
path_nodes = set(path_nodes)
# print(path_nodes)
# print(list(node_connected_pair))
# print(node_reachable)
# print("paths:", paths)
# print(set(paths))
digraph = graph.to_directed()
digraph.add_node(source, source=True)
digraph.add_nodes_from(gu.set_diff(digraph.nodes(), [source]), source=False)
digraph.add_nodes_from(node_reachable, reachable=True)
digraph.add_nodes_from(gu.set_diff(digraph.nodes(), node_reachable), reachable=False)
digraph.add_nodes_from(gu.set_diff(digraph.nodes(), path_nodes), solution=False)
digraph.add_nodes_from(path_nodes, solution=True)
digraph.add_edges_from(gu.set_diff(digraph.edges(), paths), solution=False)
digraph.add_edges_from(paths, solution=True)
nodes = (digraph.nodes[n] for n in digraph)
print('nodes_list:', list(nodes))
edges = (digraph.edges[(u, v)] for u, v in digraph.edges())
print('edges_list:', list(edges))
# rand = np.random.RandomState(seed=1)
# i = rand.choice(len(node_pairs))
# print(node_pairs[i])
# start, end = node_pairs[i]
# path = nx.all_simple_paths(
# G, source=start, target=end)
#
# print(len(path))
return digraph
def test_complete(self):
for G in [self.K10, self.K5, self.K20]:
K = nx.all_pairs_node_connectivity(G)
for source in K:
for target, k in K[source].items():
assert_true(k == len(G)-1)
def test_all_pairs_connectivity_nbunch(self):
G = nx.complete_graph(5)
nbunch = [0, 2, 3]
C = nx.all_pairs_node_connectivity(G, nbunch=nbunch)
assert_equal(len(C), len(nbunch))
def __init__(self,
img_skel,
img_dist,
units=1,
near_node_tol=5,
length_tol=1,
min_nodes=3,
plot=False,
img_enhanced=np.zeros(0),
output_dir='',
name='',
save_files=True):
self.units = units
self.img_skel = img_skel
self.img_dist = img_dist
self.near_node_tol = near_node_tol
self.length_tol = length_tol
self.min_nodes = min_nodes
self.img_enhanced = img_enhanced
self.get_ends_and_branches()
self.create_graph_and_nodes()
self.fill_edges()
self.combine_near_nodes_eculid()
self.combine_near_nodes_length()
self.remove_zero_length_edges()
self.remove_self_loops()
self.remove_small_subgraphs()
for n in self.G.nodes():
if len(self.G.edges(n)) > 1:
self.total_branch_points += 1
elif len(self.G.edges(n)) == 1:
self.total_ends += 1
else:
print(n, self.G.edges(n))
self.get_tots_and_hist()
self.connectivity = []
all_connectivity = nx.all_pairs_node_connectivity(self.G)
for n1 in all_connectivity:
for n2 in all_connectivity[n1]:
if all_connectivity[n1][n2] > 0:
self.connectivity.append(all_connectivity[n1][n2])
self.get_pos_dict()
save_path = ''
if save_files:
if not os.path.isdir(output_dir + 'networks/'):
os.mkdir(output_dir + 'networks/')
if not os.path.isdir(output_dir + 'networks/' + name + '/'):
os.mkdir(output_dir + 'networks/' + name + '/')
save_path = output_dir + 'networks/' + name + '/'
nx.write_gpickle(self.G, save_path + 'Graph.gpickle')
if plot or save_files:
self.show_graph(with_pos=self.pos,
with_background=img_enhanced,
with_skel=morphology.dilation(self.img_skel),
save=save_files,
save_path=save_path + 'with_background.png',
show=plot)
self.show_graph(save=save_files,
save_path=save_path + 'without_background.png',
show=plot)
# x = pickle.load(f).astype(numpy.float32)
# with open('tfidf_all_pred3_0509.pkl', 'rb') as f:
# x = pickle.load(f).astype(numpy.float32)
# with open('tfidf_all_pred2_0506.pkl', 'rb') as f:
# x = pickle.load(f).astype(numpy.float32)
with open('tfidf_all_pred2_0512.pkl', 'rb') as f:
x = pickle.load(f).astype(numpy.float32)
df['pred'] = x
avg_pos = df[df['is_duplicate'] == 1]['pred'].mean()
avg_neg = df[df['is_duplicate'] == 0]['pred'].mean()
import networkx as nx
numpy.random.seed(111)
G = nx.Graph()
edges = [tuple(x) for x in df[['question1', 'question2', 'pred']].values]
G.add_weighted_edges_from(edges)
map_dist = nx.all_pairs_node_connectivity(G)
list_dist = []
for q1, q2 in tqdm(df[['question1', 'question2']].values):
list_dist.append(map_dist[q1][q2])
list_dist = numpy.array(list_dist)
with open('train_dist.pkl', 'wb') as f:
pickle.dump(list_dist, f, -1)
