def evaluateDynamic_changed_LinkPrediction_v2(graph,
embedding,
rounds,
edges_add,
edges_rm,
n_sample_nodes=None,
no_python=False,
is_undirected=True,
sampling_scheme="u_rand"):
nodes = []
for e in edges_add[0]:
nodes.append(e[0])
nodes.append(e[1])
# for e in edges_rm[0]:
# nodes.append(e[0])
# nodes.append(e[1])
nodes = list(np.unique(nodes))
# pdb.set_trace()
test_digraph, node_dict = graph_util.sample_graph_nodes(graph, nodes)
estimated_adj = embedding.predict_next_adj(node_l)
predicted_edge_list = evaluation_util.getEdgeListFromAdjMtx(
estimated_adj, is_undirected=is_undirected, edge_pairs=None)
MAP = metrics.computeMAP(predicted_edge_list, test_digraph, node_dict,
edges_rm)
node_edges_rm = []
for i in range(len(edges_rm[0])):
node_edges_rm.append([])
for st, ed in edges_rm[0]:
node_edges_rm[node_dict[st]].append((node_dict[st], node_dict[ed], 1))
node_edges_rm = [
node_edges_rm[i] for i in xrange(len(node_edges_rm))
if len(node_edges_rm[i]) > 0
]
# pdb.set_trace()
prec_curv, _ = metrics.computePrecisionCurve(predicted_edge_list,
test_digraph, node_edges_rm)
# pdb.set_trace()
return (MAP, prec_curv)
graphs.append(
nx.read_gpickle('./test_data/academic/pickle/' + str(i)))
G_cen = nx.degree_centrality(
graphs[29]) # graph 29 in academia has highest number of edges
G_cen = sorted(G_cen.items(), key=operator.itemgetter(1), reverse=True)
node_l = []
i = 0
while i < sample:
node_l.append(G_cen[i][0])
i += 1
# pdb.set_trace()
# node_l = np.random.choice(range(graphs[29].number_of_nodes()), 5000, replace=False)
# print(node_l)
for i in range(length):
graphs[i] = graph_util.sample_graph_nodes(graphs[i], node_l)
# pdb.set_trace()
graphs = graphs[-args.timelength:]
datafile = dataprep_util.prep_input_TIMERS(graphs, args.timelength,
args.testDataType)
embedding = TIMERS(K=dim_emb,
Theta=theta,
datafile=datafile,
length=args.timelength,
nodemigration=args.nodemigration,
resultdir=args.resultdir,
datatype=args.testDataType)
outdir_tmp = './output'
if not os.path.exists(outdir_tmp):
def evaluateDynamic_changed_LinkPrediction_v2(graph,
embedding,
rounds,
edges_add,
edges_rm,
n_sample_nodes=None,
no_python=False,
is_undirected=True,
sampling_scheme="u_rand"):
"""Function to evaluate dynamic changed link prediction
Attributes:
graph (Object): Networkx Graph Object
embedding (object): Algorithm for learning graph embedding.
edges_add (list): list of edges to be added.
edges_rm (list): list of edges to be removed.
n_sampled_nodes (int): List of sampled nodes.
train_ratio_init (float): sample to be used for training and testing.
rounds (int): Number of times to run the experiment
m_summ (str): summary to be used to save the result.
is_undirected (bool): Flag to denote if the graph is directed.
sampling_scheme(str): sampling scheme for selecting the nodes.
Returns:
ndarray: Mean Average precision
"""
nodes = []
for e in edges_add[0]:
nodes.append(e[0])
nodes.append(e[1])
# for e in edges_rm[0]:
# nodes.append(e[0])
# nodes.append(e[1])
nodes = list(np.unique(nodes))
# pdb.set_trace()
test_digraph, node_dict = graph_util.sample_graph_nodes(graph, nodes)
estimated_adj = embedding.predict_next_adj(node_l)
predicted_edge_list = evaluation_util.getEdgeListFromAdjMtx(
estimated_adj, is_undirected=is_undirected, edge_pairs=None)
MAP = metrics.computeMAP(predicted_edge_list, test_digraph, node_dict,
edges_rm)
node_edges_rm = []
for i in range(len(edges_rm[0])):
node_edges_rm.append([])
for st, ed in edges_rm[0]:
node_edges_rm[node_dict[st]].append((node_dict[st], node_dict[ed], 1))
node_edges_rm = [
node_edges_rm[i] for i in xrange(len(node_edges_rm))
if len(node_edges_rm[i]) > 0
]
# pdb.set_trace()
prec_curv, _ = metrics.computePrecisionCurve(predicted_edge_list,
test_digraph, node_edges_rm)
# pdb.set_trace()
return (MAP, prec_curv)
