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 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 evaluateStaticGraphReconstruction(digraph,
graph_embedding,
X_stat,
node_l=None,
sample_ratio_e=None,
file_suffix=None,
is_undirected=True,
is_weighted=False):
"""Function to evaluate static graph reconstruction
Attributes:
digraph (Object): Networkx Graph Object
graph_embedding (object): Algorithm for learning graph embedding
X_stat (ndarray): Embedding values of the graph.
node_l (int): Total number of nodes.
sammple_ratio_e (float): SAmpling ration for testing. Only sample number of nodes are tested.
file_suffix (str): Suffix for file name.
is_undirected (bool): Flag to denote if the graph is directed.
is_weighted (bool): Flag denoting if the graph has weighted edge.
Returns:
ndarray: MAP, precision curve, error values and error baselines
"""
node_num = digraph.number_of_nodes()
# evaluation
if sample_ratio_e:
eval_edge_pairs = evaluation_util.getRandomEdgePairs(
node_num, sample_ratio_e, is_undirected)
else:
eval_edge_pairs = None
if file_suffix is None:
estimated_adj = graph_embedding.get_reconstructed_adj(X_stat, node_l)
else:
estimated_adj = graph_embedding.get_reconstructed_adj(
X_stat, node_l, file_suffix)
predicted_edge_list = evaluation_util.getEdgeListFromAdjMtx(
estimated_adj, is_undirected=is_undirected, edge_pairs=eval_edge_pairs)
MAP = metrics.computeMAP(predicted_edge_list, digraph)
prec_curv, _ = metrics.computePrecisionCurve(predicted_edge_list, digraph)
# If weighted, compute the error in reconstructed weights of observed edges
if is_weighted:
digraph_adj = nx.to_numpy_matrix(digraph)
estimated_adj[digraph_adj == 0] = 0
err = np.linalg.norm(digraph_adj - estimated_adj)
err_baseline = np.linalg.norm(digraph_adj)
else:
err = None
err_baseline = None
return (MAP, prec_curv, err, err_baseline)
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)