def evaluateDynamic_changed_LinkPrediction(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_l = graph_util.sample_graph(graph, len(nodes), 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)
prec_curv, _ = metrics.computePrecisionCurve(predicted_edge_list,
test_digraph)
return (MAP, prec_curv)
def evaluateDynamicLinkPrediction_TIMERS(graph,
embedding,
t,
rounds,
n_sample_nodes=None,
no_python=False,
is_undirected=True,
sampling_scheme="u_rand"):
node_l = None
if n_sample_nodes:
if sampling_scheme == "u_rand":
test_digraph, node_l = graph_util.sample_graph(
graph, n_sample_nodes)
else:
test_digraph, node_l = graph_util.sample_graph_rw_int(
graph, n_sample_nodes)
estimated_adj = embedding.predict_next_adj(t, 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)
prec_curv, _ = metrics.computePrecisionCurve(predicted_edge_list,
test_digraph)
return (MAP, prec_curv)
def evaluateDynamicLinkPrediction(graph,
embedding,
rounds,
n_sample_nodes=None,
no_python=False,
is_undirected=True,
sampling_scheme="u_rand"):
node_l = None
if n_sample_nodes:
if sampling_scheme == "u_rand":
test_digraph, node_l = graph_util.sample_graph(
graph,
n_sample_nodes
)
else:
test_digraph, node_l = graph_util.sample_graph_rw_int(
graph,
n_sample_nodes
)
else:
test_digraph, node_l = graph_util.sample_graph(
graph,
n_sample_nodes)
estimated_adj = embedding.predict_next_adj(node_l)
print(len(estimated_adj), np.shape(estimated_adj))
predicted_edge_list = evaluation_util.getEdgeListFromAdjMtx(
estimated_adj,
is_undirected=is_undirected,
edge_pairs=None
)
print(len(predicted_edge_list), np.shape(predicted_edge_list), len(test_digraph.edges()),
np.shape(test_digraph.edges()))
# pdb.set_trace()
MAP = computeMAP(predicted_edge_list, test_digraph)
prec_curv, _ = computePrecisionCurve(
predicted_edge_list,
test_digraph
)
return (MAP, prec_curv)
def expGR(digraph,
graph_embedding,
X,
n_sampled_nodes,
rounds,
res_pre,
m_summ,
file_suffix=None,
is_undirected=True,
sampling_scheme="rw"):
print('\tGraph Reconstruction')
n_sampled_nodes = int(n_sampled_nodes)
summ_file = open('%s_%s.grsumm' % (res_pre, m_summ), 'w')
summ_file.write('Method\t%s\n' % metrics.getMetricsHeader())
if digraph.number_of_nodes() <= n_sampled_nodes:
rounds = 1
MAP = [None] * rounds
prec_curv = [None] * rounds
err = [None] * rounds
err_b = [None] * rounds
n_nodes = [None] * rounds
n_edges = [None] * rounds
for round_id in range(rounds):
if sampling_scheme == "u_rand":
sampled_digraph, node_l = graph_util.sample_graph(
digraph, n_sampled_nodes=n_sampled_nodes)
else:
sampled_digraph, node_l = graph_util.sample_graph_rw_int(
digraph, n_sampled_nodes=n_sampled_nodes)
n_nodes[round_id] = sampled_digraph.number_of_nodes()
n_edges[round_id] = sampled_digraph.number_of_edges()
print('\t\tRound: %d, n_nodes: %d, n_edges:%d\n' %
(round_id, n_nodes[round_id], n_edges[round_id]))
sampled_X = X[node_l]
# sampled_X = np.expand_dims(sampled_X,axis=1)
MAP[round_id], prec_curv[round_id], err[round_id], err_b[round_id] = \
evaluateStaticGraphReconstruction(sampled_digraph,
graph_embedding,
sampled_X,
node_l,
file_suffix= file_suffix,
is_undirected=is_undirected
)
try:
summ_file.write('Err: %f/%f\n' % (np.mean(err), np.std(err)))
summ_file.write('Err_b: %f/%f\n' % (np.mean(err_b), np.std(err_b)))
except TypeError:
pass
summ_file.write('%f/%f\t%s\n' %
(np.mean(MAP), np.std(MAP),
metrics.getPrecisionReport(prec_curv[0], n_edges[0])))
pickle.dump([n_nodes, n_edges, MAP, prec_curv, err, err_b],
open('%s_%s.gr' % (res_pre, m_summ), 'wb'))
return np.mean(np.array(MAP))
def evaluateDynamic_changed_LinkPrediction(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_l = graph_util.sample_graph(graph, len(nodes), 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)
prec_curv, _ = metrics.computePrecisionCurve(predicted_edge_list,
test_digraph)
return (MAP, prec_curv)
def evaluateDynamicLinkPrediction(graph,
embedding,
rounds,
n_sample_nodes=None,
no_python=False,
is_undirected=True,
sampling_scheme="u_rand"):
"""Function to evaluate Dynamic Link Prediction
Attributes:
graph (Object): Networkx Graph Object
embedding (object): Algorithm for learning graph embedding
n_sample_nodes (list): sampled nodes
is_undirected (bool): Flag to denote if the graph is directed.
sampling_scheme (str): Sampling scheme to be used.
Returns:
ndarray: MAP, precision curve
"""
node_l = None
if n_sample_nodes:
if sampling_scheme == "u_rand":
test_digraph, node_l = graph_util.sample_graph(
graph, n_sample_nodes)
else:
test_digraph, node_l = graph_util.sample_graph_rw_int(
graph, n_sample_nodes)
estimated_adj = embedding.predict_next_adj(node_l)
print(len(estimated_adj), np.shape(estimated_adj))
predicted_edge_list = evaluation_util.getEdgeListFromAdjMtx(
estimated_adj, is_undirected=is_undirected, edge_pairs=None)
print(len(predicted_edge_list), np.shape(predicted_edge_list),
len(test_digraph.edges()), np.shape(test_digraph.edges()))
# pdb.set_trace()
MAP = metrics.computeMAP(predicted_edge_list, test_digraph)
prec_curv, _ = metrics.computePrecisionCurve(predicted_edge_list,
test_digraph)
return (MAP, prec_curv)
def expGR(digraph,
graph_embedding,
X,
n_sampled_nodes,
rounds,
res_pre,
m_summ,
file_suffix=None,
is_undirected=True,
sampling_scheme="rw"):
"""Function to evaluate graph reconstruction
Attributes:
digraph (Object): Networkx Graph Object
graph_embedding (object): Algorithm for learning graph embedding
X_stat (ndarray): Embedding values of the graph.
n_sampled_nodes (int): Total number of nodes.
rounds (int): Number of times to run the experiment
res_pre (str): prefix to be used to store the result.
m_summ (str): summary to be used to save the result.
file_suffix (str): Suffix for file name.
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
"""
print('\tGraph Reconstruction')
n_sampled_nodes = int(n_sampled_nodes)
summ_file = open('%s_%s.grsumm' % (res_pre, m_summ), 'w')
summ_file.write('Method\t%s\n' % metrics.getMetricsHeader())
if digraph.number_of_nodes() <= n_sampled_nodes:
rounds = 1
MAP = [None] * rounds
prec_curv = [None] * rounds
err = [None] * rounds
err_b = [None] * rounds
n_nodes = [None] * rounds
n_edges = [None] * rounds
for round_id in range(rounds):
if sampling_scheme == "u_rand":
sampled_digraph, node_l = graph_util.sample_graph(
digraph, n_sampled_nodes=n_sampled_nodes)
else:
sampled_digraph, node_l = graph_util.sample_graph_rw_int(
digraph, n_sampled_nodes=n_sampled_nodes)
n_nodes[round_id] = sampled_digraph.number_of_nodes()
n_edges[round_id] = sampled_digraph.number_of_edges()
print('\t\tRound: %d, n_nodes: %d, n_edges:%d\n' %
(round_id, n_nodes[round_id], n_edges[round_id]))
sampled_X = X[node_l]
# sampled_X = np.expand_dims(sampled_X,axis=1)
MAP[round_id], prec_curv[round_id], err[round_id], err_b[round_id] = \
evaluateStaticGraphReconstruction(sampled_digraph,
graph_embedding,
sampled_X,
node_l,
file_suffix= file_suffix,
is_undirected=is_undirected
)
try:
summ_file.write('Err: %f/%f\n' % (np.mean(err), np.std(err)))
summ_file.write('Err_b: %f/%f\n' % (np.mean(err_b), np.std(err_b)))
except TypeError:
pass
summ_file.write('%f/%f\t%s\n' %
(np.mean(MAP), np.std(MAP),
metrics.getPrecisionReport(prec_curv[0], n_edges[0])))
pickle.dump([n_nodes, n_edges, MAP, prec_curv, err, err_b],
open('%s_%s.gr' % (res_pre, m_summ), 'wb'))
return np.mean(np.array(MAP))