def expstatic_changedLP(dynamic_sbm_series,
graphs,
embedding,
rounds,
res_pre,
m_summ,
n_sample_nodes=1000,
train_ratio_init=0.5,
no_python=False,
is_undirected=True,
sampling_scheme="u_rand"):
n_sample_nodes = int(n_sample_nodes)
print('\tDynamic Link Prediction')
summ_file = open('%s%s.dlpsumm' % (res_pre, m_summ), 'w')
summ_file.write('Method\t%s\n' % metrics.getMetricsHeader())
summ_file.close()
T = len(graphs)
# T_min = int(train_ratio_init * T)
MAP = [None] * (T - 1)
prec_curv = [None] * (T - 1)
for i in range(T - 1):
MAP[i] = [None] * rounds
prec_curv[i] = [None] * rounds
for t in range(T - 1):
embedding.learn_embeddings(graphs[t])
edges_add, edges_rm = getchangedlinks(graphs[t], graphs[t + 1])
for r_id in range(rounds):
MAP[t][r_id], prec_curv[t][r_id] = \
evaluateDynamic_changed_LinkPrediction(graphs[t + 1], embedding,
rounds,
edges_add, edges_rm,
# dynamic_sbm_series[t][3],
n_sample_nodes=n_sample_nodes,
no_python=no_python,
is_undirected=is_undirected,
sampling_scheme=sampling_scheme)
summ_file = open('%s%s.dlpsumm' % (res_pre, m_summ), 'a')
summ_file.write('\tt=%d%f/%f\t%s\n' %
(t, np.mean(MAP[t]), np.std(MAP[t]),
metrics.getPrecisionReport(prec_curv[t][0],
len(prec_curv[t][0]))))
summ_file.close()
# pickle.dump([MAP, prec_curv],
# open('%s_%s_%s.lp' % (res_pre, m_summ, sampling_scheme),
# 'wb'))
return np.mean(np.array(MAP))
def expLP(graphs,
embedding,
rounds,
res_pre,
m_summ,
n_sample_nodes=1000,
train_ratio_init=0.5,
no_python=False,
is_undirected=True,
sampling_scheme="u_rand"):
"""Function to evaluate link prediction
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): 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
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
"""
n_sample_nodes = int(n_sample_nodes)
print('\tDynamic Link Prediction')
summ_file = open('%s%s.dlpsumm' % (res_pre, m_summ), 'w')
summ_file.write('Method\t%s\n' % metrics.getMetricsHeader())
summ_file.close()
T = len(graphs)
T_min = int(train_ratio_init * T)
MAP = [None] * (T - T_min)
prec_curv = [None] * (T - T_min)
for i in range(T - T_min):
MAP[i] = [None] * rounds
prec_curv[i] = [None] * rounds
for t in range(T_min, T):
embedding.learn_embeddings(graphs[:t])
for r_id in range(rounds):
MAP[t - T_min][r_id], prec_curv[t - T_min][r_id] = \
evaluateDynamicLinkPrediction(graphs[t], embedding,
rounds,
n_sample_nodes=n_sample_nodes,
no_python=no_python,
is_undirected=is_undirected,
sampling_scheme=sampling_scheme)
summ_file = open('%s%s.dlpsumm' % (res_pre, m_summ), 'a')
summ_file.write(
'\tt=%d%f/%f\t%s\n' %
(t - T_min, np.mean(MAP[t - T_min]), np.std(MAP[t - T_min]),
metrics.getPrecisionReport(prec_curv[t - T_min][0],
len(prec_curv[t - T_min][0]))))
summ_file.close()
# pickle.dump([MAP, prec_curv],
# open('%s_%s_%s.lp' % (res_pre, m_summ, sampling_scheme),
# 'wb'))
return np.mean(np.array(MAP))
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))