def run(self):
saved_targets = self.settings.parsed_args.target
self.settings.parsed_args.target = ['.']
ret = super(CmdShowPipeline, self).run()
self.settings.parsed_args.target = saved_targets
if ret != 0:
Logger.error('Failed to build dependency graph for the project')
return 1
# Try to find independent clusters which might occure
# when a bunch of data items were used independently.
self.subs = nx.weakly_connected_component_subgraphs(self.g)
return self.draw_targets(saved_targets)
python类weakly_connected_component_subgraphs()的实例源码
def get_largest_component(G, strongly=False):
"""
Return the largest weakly or strongly connected component from a directed
graph.
Parameters
----------
G : networkx multidigraph
strongly : bool
if True, return the largest strongly instead of weakly connected
component
Returns
-------
networkx multidigraph
"""
start_time = time.time()
original_len = len(list(G.nodes()))
if strongly:
# if the graph is not connected and caller did not request retain_all,
# retain only the largest strongly connected component
if not nx.is_strongly_connected(G):
G = max(nx.strongly_connected_component_subgraphs(G), key=len)
msg = ('Graph was not connected, retained only the largest strongly '
'connected component ({:,} of {:,} total nodes) in {:.2f} seconds')
log(msg.format(len(list(G.nodes())), original_len, time.time()-start_time))
else:
# if the graph is not connected and caller did not request retain_all,
# retain only the largest weakly connected component
if not nx.is_weakly_connected(G):
G = max(nx.weakly_connected_component_subgraphs(G), key=len)
msg = ('Graph was not connected, retained only the largest weakly '
'connected component ({:,} of {:,} total nodes) in {:.2f} seconds')
log(msg.format(len(list(G.nodes())), original_len, time.time()-start_time))
return G
def get_lcc(di_graph):
di_graph = max(nx.weakly_connected_component_subgraphs(di_graph), key=len)
tdl_nodes = di_graph.nodes()
nodeListMap = dict(zip(tdl_nodes, range(len(tdl_nodes))))
nx.relabel_nodes(di_graph, nodeListMap, copy=False)
return di_graph, nodeListMap
def main():
G, karmas = read_data("karma.txt")
cs = nx.weakly_connected_component_subgraphs(G)
cs.sort(key=lambda c: c.number_of_nodes(), reverse=True)
plt.clf()
draw(cs[126], karmas)
plt.show()
def get_largest_component(graph):
"""Gets the giant component of a subgraph
:param pybel.BELGraph graph: A BEL Graph
:return: The giant component of the graph
:rtype: pybel.BELGraph
"""
return max(nx.weakly_connected_component_subgraphs(graph), key=len)
def evaluateStaticLinkPrediction(digraph, graph_embedding,
train_ratio=0.8,
n_sample_nodes=None,
sample_ratio_e=None,
no_python=False,
is_undirected=True):
node_num = digraph.number_of_nodes()
# seperate train and test graph
train_digraph, test_digraph = evaluation_util.splitDiGraphToTrainTest(
digraph,
train_ratio=train_ratio,
is_undirected=is_undirected
)
if not nx.is_connected(train_digraph.to_undirected()):
train_digraph = max(
nx.weakly_connected_component_subgraphs(train_digraph),
key=len
)
tdl_nodes = train_digraph.nodes()
nodeListMap = dict(zip(tdl_nodes, range(len(tdl_nodes))))
nx.relabel_nodes(train_digraph, nodeListMap, copy=False)
test_digraph = test_digraph.subgraph(tdl_nodes)
nx.relabel_nodes(test_digraph, nodeListMap, copy=False)
# learning graph embedding
X, _ = graph_embedding.learn_embedding(
graph=train_digraph,
no_python=no_python
)
node_l = None
if n_sample_nodes:
test_digraph, node_l = graph_util.sample_graph(
test_digraph,
n_sample_nodes
)
X = X[node_l]
# evaluation
if sample_ratio_e:
eval_edge_pairs = evaluation_util.getRandomEdgePairs(
node_num,
sample_ratio_e,
is_undirected
)
else:
eval_edge_pairs = None
estimated_adj = graph_embedding.get_reconstructed_adj(X, node_l)
predicted_edge_list = evaluation_util.getEdgeListFromAdjMtx(
estimated_adj,
is_undirected=is_undirected,
edge_pairs=eval_edge_pairs
)
filtered_edge_list = [e for e in predicted_edge_list if not train_digraph.has_edge(e[0], e[1])]
MAP = metrics.computeMAP(filtered_edge_list, test_digraph)
prec_curv, _ = metrics.computePrecisionCurve(
filtered_edge_list,
test_digraph
)
return (MAP, prec_curv)
