def node_betweeness_centrality(X):
"""
based on networkx function: betweenness_centrality
"""
XX = np.zeros((X.shape[0], np.sqrt(X.shape[1])))
for i, value in enumerate(X):
adj_mat = value.reshape((np.sqrt(len(value)), -1))
adj_mat = (adj_mat - np.min(adj_mat)) / (np.max(adj_mat) - np.min(adj_mat))
adj_mat = 1 - adj_mat
# th = np.mean(adj_mat) - 0.1
# adj_mat = np.where(adj_mat < th, adj_mat, 0.)
percent, th, adj_mat, triu = percentage_removed(adj_mat, 0.6) #34
print("percent = {0}, threshold position = {1}, threshold = {2}\n".format(percent, th, triu[th]))
g = nx.from_numpy_matrix(adj_mat)
print "Graph Nodes = {0}, Graph Edges = {1} ".format(g.number_of_nodes(), g.number_of_edges())
print "\nEdge kept ratio, {0}".format(float(g.number_of_edges())/((g.number_of_nodes()*(g.number_of_nodes()-1))/2))
deg_cent = nx.betweenness_centrality(g)
# deg_cent = nx.betweenness_centrality(g)*1000
node_cent = np.zeros(g.number_of_nodes())
for k in deg_cent:
node_cent[k] = deg_cent[k]
# node_cent = deg_cent.values()
XX[i] = node_cent
print "graph {0} => mean {1}, min {2}, max {3}".format(i, np.mean(XX[i]), np.min(XX[i]), np.max(XX[i]))
# XX = XX*100
XX = StandardScaler().fit_transform(XX.T).T
return XX
def node_closeness_centrality(X):
"""
based on networkx function: closeness_centrality
"""
XX = np.zeros((X.shape[0], np.sqrt(X.shape[1])))
for i, value in enumerate(X):
adj_mat = value.reshape((np.sqrt(len(value)), -1))
adj_mat = (adj_mat - np.min(adj_mat)) / (np.max(adj_mat) - np.min(adj_mat))
adj_mat = 1 - adj_mat
# th = np.mean(adj_mat) - 0.23
# adj_mat = np.where(adj_mat < th, adj_mat, 0.)
percent, th, adj_mat, triu = percentage_removed(adj_mat, 0.27) # in this context the percentage
print("percent = {0}, threshold position = {1}, threshold = {2}\n".format(percent, th, triu[th]))
g = nx.from_numpy_matrix(adj_mat)
print "Graph Nodes = {0}, Graph Edges = {1} ".format(g.number_of_nodes(), g.number_of_edges())
print "\nEdge kept ratio, {0}".format(float(g.number_of_edges())/((g.number_of_nodes()*(g.number_of_nodes()-1))/2))
deg_cent = nx.closeness_centrality(g, normalized=True)
node_cent = np.zeros(g.number_of_nodes())
for k in deg_cent:
node_cent[k] = deg_cent[k]
XX[i] = node_cent
print "graph {0} => mean {1}, min {2}, max {3}".format(i, np.mean(XX[i]), np.min(XX[i]), np.max(XX[i]))
return XX
def node_centrality(X):
"""
based on networkx function: degree_centrality
"""
# graph_position = np.arange(0, 192, 48)
# pos_counter = 0
XX = np.zeros((X.shape[0], np.sqrt(X.shape[1])))
for i, value in enumerate(X):
adj_mat = value.reshape(
(np.sqrt(len(value)), -1)) # reshape to 254x254
adj_mat = (adj_mat - np.min(adj_mat)) / (np.max(adj_mat) -
np.min(adj_mat))
adj_mat = 1 - adj_mat
thres = None
percent, th, adj_mat, triu = percentage_removed(
adj_mat, 0.9945, thres, "node_centrality") #0.98945
print("percent = {0}, threshold position = {1}, threshold = {2}\n".
format(percent, th, triu[th]))
# th = np.mean(adj_mat)
# th = np.mean(adj_mat) + 0.22 #22
# adj_mat = np.where(adj_mat > th, 1., 0.) # binary adjacency matrix
g = nx.from_numpy_matrix(adj_mat)
print "Graph Nodes = {0}, Graph Edges = {1} ".format(
g.number_of_nodes(), g.number_of_edges())
print "Edge kept ratio, {0}".format(
float(g.number_of_edges()) / ((g.number_of_nodes() *
(g.number_of_nodes() - 1)) / 2))
# graph plotting
# if (pos_counter > 3): pos_counter = 0
# nfigs = False
# from_graph = 4
# to_graph = 8
# graph_plot(g, i, graph_position[pos_counter], from_graph, to_graph, nfigs, \
# subplot = True)
# pos_counter += 1
deg_cent = nx.degree_centrality(g)
node_cent = np.zeros(g.number_of_nodes())
for k in deg_cent:
node_cent[k] = deg_cent[k]
XX[i] = node_cent
print "graph {0} => mean {1}, min {2}, max {3}\n".format(
i, np.mean(XX[i]), np.min(XX[i]), np.max(XX[i]))
return XX
def node_betweeness_centrality(X):
"""
based on networkx function: betweenness_centrality
"""
XX = np.zeros((X.shape[0], np.sqrt(X.shape[1])))
for i, value in enumerate(X):
adj_mat = value.reshape((np.sqrt(len(value)), -1))
adj_mat = (adj_mat - np.min(adj_mat)) / (np.max(adj_mat) -
np.min(adj_mat))
adj_mat = 1 - adj_mat
# th = np.mean(adj_mat) - 0.1
# adj_mat = np.where(adj_mat < th, adj_mat, 0.)
percent, th, adj_mat, triu = percentage_removed(adj_mat, 0.6) #34
print("percent = {0}, threshold position = {1}, threshold = {2}\n".
format(percent, th, triu[th]))
g = nx.from_numpy_matrix(adj_mat)
print "Graph Nodes = {0}, Graph Edges = {1} ".format(
g.number_of_nodes(), g.number_of_edges())
print "\nEdge kept ratio, {0}".format(
float(g.number_of_edges()) / ((g.number_of_nodes() *
(g.number_of_nodes() - 1)) / 2))
deg_cent = nx.betweenness_centrality(g)
# deg_cent = nx.betweenness_centrality(g)*1000
node_cent = np.zeros(g.number_of_nodes())
for k in deg_cent:
node_cent[k] = deg_cent[k]
# node_cent = deg_cent.values()
XX[i] = node_cent
print "graph {0} => mean {1}, min {2}, max {3}".format(
i, np.mean(XX[i]), np.min(XX[i]), np.max(XX[i]))
# XX = XX*100
XX = StandardScaler().fit_transform(XX.T).T
return XX
def node_centrality(X):
"""
based on networkx function: degree_centrality
"""
# graph_position = np.arange(0, 192, 48)
# pos_counter = 0
XX = np.zeros((X.shape[0], np.sqrt(X.shape[1])))
for i, value in enumerate(X):
adj_mat = value.reshape((np.sqrt(len(value)), -1)) # reshape to 254x254
adj_mat = (adj_mat - np.min(adj_mat)) / (np.max(adj_mat) - np.min(adj_mat))
adj_mat = 1 - adj_mat
thres = None
percent, th, adj_mat, triu = percentage_removed(adj_mat, 0.9945, thres, "node_centrality") #0.98945
print("percent = {0}, threshold position = {1}, threshold = {2}\n".format(percent, th, triu[th]))
# th = np.mean(adj_mat)
# th = np.mean(adj_mat) + 0.22 #22
# adj_mat = np.where(adj_mat > th, 1., 0.) # binary adjacency matrix
g = nx.from_numpy_matrix(adj_mat)
print "Graph Nodes = {0}, Graph Edges = {1} ".format(g.number_of_nodes(), g.number_of_edges())
print "Edge kept ratio, {0}".format(float(g.number_of_edges())/((g.number_of_nodes()*(g.number_of_nodes()-1))/2))
# graph plotting
# if (pos_counter > 3): pos_counter = 0
# nfigs = False
# from_graph = 4
# to_graph = 8
# graph_plot(g, i, graph_position[pos_counter], from_graph, to_graph, nfigs, \
# subplot = True)
# pos_counter += 1
deg_cent = nx.degree_centrality(g)
node_cent = np.zeros(g.number_of_nodes())
for k in deg_cent:
node_cent[k] = deg_cent[k]
XX[i] = node_cent
print "graph {0} => mean {1}, min {2}, max {3}\n".format(i, np.mean(XX[i]), np.min(XX[i]), np.max(XX[i]))
return XX
def node_closeness_centrality(X):
"""
based on networkx function: closeness_centrality
"""
XX = np.zeros((X.shape[0], np.sqrt(X.shape[1])))
for i, value in enumerate(X):
adj_mat = value.reshape((np.sqrt(len(value)), -1))
adj_mat = (adj_mat - np.min(adj_mat)) / (np.max(adj_mat) -
np.min(adj_mat))
adj_mat = 1 - adj_mat
# th = np.mean(adj_mat) - 0.23
# adj_mat = np.where(adj_mat < th, adj_mat, 0.)
percent, th, adj_mat, triu = percentage_removed(
adj_mat, 0.27) # in this context the percentage
print("percent = {0}, threshold position = {1}, threshold = {2}\n".
format(percent, th, triu[th]))
g = nx.from_numpy_matrix(adj_mat)
print "Graph Nodes = {0}, Graph Edges = {1} ".format(
g.number_of_nodes(), g.number_of_edges())
print "\nEdge kept ratio, {0}".format(
float(g.number_of_edges()) / ((g.number_of_nodes() *
(g.number_of_nodes() - 1)) / 2))
deg_cent = nx.closeness_centrality(g, normalized=True)
node_cent = np.zeros(g.number_of_nodes())
for k in deg_cent:
node_cent[k] = deg_cent[k]
XX[i] = node_cent
print "graph {0} => mean {1}, min {2}, max {3}".format(
i, np.mean(XX[i]), np.min(XX[i]), np.max(XX[i]))
return XX