def test_K4_normalized(self):
"""Betweenness centrality: K4"""
G = nx.complete_graph(4)
b = nx.current_flow_betweenness_centrality(G, normalized=True)
b_answer = {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
G.add_edge(0, 1, weight=0.5, other=0.3)
b = nx.current_flow_betweenness_centrality(G,
normalized=True,
weight=None)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
wb_answer = {0: 0.2222222, 1: 0.2222222, 2: 0.30555555, 3: 0.30555555}
b = nx.current_flow_betweenness_centrality(G,
normalized=True,
weight="weight")
for n in sorted(G):
assert almost_equal(b[n], wb_answer[n])
wb_answer = {0: 0.2051282, 1: 0.2051282, 2: 0.33974358, 3: 0.33974358}
b = nx.current_flow_betweenness_centrality(G,
normalized=True,
weight="other")
for n in sorted(G):
assert almost_equal(b[n], wb_answer[n])
def test_K4(self):
"""Betweenness centrality: K4"""
G=networkx.complete_graph(4)
b=networkx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True)
b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
# test weighted network
G.add_edge(0,1,{'weight':0.5,'other':0.3})
b=networkx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True,
weight=None)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
b=networkx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True)
b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
b=networkx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True,
weight='other')
b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True,weight='other')
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_P4(self):
"""Betweenness centrality: P4"""
G = nx.path_graph(4)
b = nx.current_flow_betweenness_centrality(G, normalized=False)
b_answer = {0: 0, 1: 2, 2: 2, 3: 0}
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
def test_P4_normalized(self):
"""Betweenness centrality: P4 normalized"""
G = networkx.path_graph(4)
b = networkx.current_flow_betweenness_centrality(G, normalized=True)
b_answer = {0: 0, 1: 2. / 3, 2: 2. / 3, 3: 0}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def agregar_centralidad(G, criterio='degree', max_iter=100, tol=1e-3):
if criterio == 'degree':
nodes = list(G.nodes())
degree = list(dict(nx.degree(G)).values())
return nodes, degree
if criterio == 'eigen':
nodes = list(G.nodes())
eigen = list(
dict(nx.eigenvector_centrality(G, max_iter=max_iter,
tol=tol)).values())
return nodes, eigen
if criterio == 'sub':
nodes = list(G.nodes())
sub = list(dict(nx.subgraph_centrality(G)).values())
return nodes, sub
if criterio == 'bet':
nodes = list(G.nodes())
bet = list(dict(nx.betweenness_centrality(G)).values())
return nodes, bet
if criterio == 'flow':
nodes = list(G.nodes())
flow = list(dict(nx.current_flow_betweenness_centrality(G)).values())
return nodes, flow
if criterio == 'random':
nodes = list(G.nodes())
value = random.choice(nodes)
return value, nodes
def test_K4_normalized(self):
"""Betweenness centrality: K4"""
G = networkx.complete_graph(4)
b = networkx.current_flow_betweenness_centrality_subset(G, G.nodes(), G.nodes(), normalized=True)
b_answer = networkx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def compute_centrality(star_dict, edge_dict):
#build up a nx graph
galaxy = networkx.Graph()
for v, vertex in star_dict.iteritems():
galaxy.add_node(v)
for v, neighbors in edge_dict.iteritems():
for n in neighbors:
galaxy.add_edge(v,n)
print "betweenness"
betweenness_map = networkx.current_flow_betweenness_centrality(galaxy)
betweenness_map = normalize(betweenness_map)
for key, value in betweenness_map.iteritems():
star_dict[key]['betweenness'] = value
print "closeness"
closeness_map = networkx.current_flow_closeness_centrality(galaxy)
closeness_map = normalize(closeness_map)
for key, value in closeness_map.iteritems():
star_dict[key]['closeness'] = value
print "pagerank"
pagerank_map = networkx.pagerank_scipy(galaxy)
pagerank_map = normalize(pagerank_map)
for key, value in pagerank_map.iteritems():
star_dict[key]['pagerank'] = value
def test_P4(self):
"""Betweenness centrality: P4"""
G=nx.path_graph(4)
b=nx.current_flow_betweenness_centrality(G,normalized=False)
b_answer={0: 0, 1: 2, 2: 2, 3: 0}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_K4(self):
"""Betweenness centrality: K4"""
G=networkx.complete_graph(4)
b=networkx.current_flow_betweenness_centrality(G,normalized=False)
b_answer={0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_K4(self):
"""Betweenness centrality: K4"""
G = networkx.complete_graph(4)
b = networkx.current_flow_betweenness_centrality(G, normalized=False)
b_answer = {0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_P4_normalized(self):
"""Betweenness centrality: P4 normalized"""
G=networkx.path_graph(4)
b=networkx.current_flow_betweenness_centrality(G,normalized=True)
b_answer={0: 0, 1: 2./3, 2: 2./3, 3:0}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_P4(self):
"""Betweenness centrality: P4"""
G = nx.path_graph(4)
b = nx.current_flow_betweenness_centrality_subset(G, list(G), list(G), normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def centrality(net):
values ={}
close = nx.closeness_centrality(net, normalized= True)
eigen = nx.eigenvector_centrality_numpy(net)
page = nx.pagerank(net)
bet = nx.betweenness_centrality(net,normalized= True)
flow_c = nx.current_flow_closeness_centrality(net,normalized= True)
flow_b = nx.current_flow_betweenness_centrality(net,normalized= True)
load = nx.load_centrality(net, normalized = True)
com_c = nx.communicability_centrality(net)
com_b = nx.communicability_betweenness_centrality(net, normalized= True)
degree = net.degree()
file3 = open("bl.csv",'w')
for xt in [bet,load,degree,page,flow_b,com_c,com_b,eigen,close,flow_c]:#[impo,bet,flow_b,load,com_c,com_b] :
for yt in [bet,load,degree,page,flow_b,com_c,com_b,eigen,close,flow_c]:#[impo,bet,flow_b,load,com_c,com_b] :
corr(xt.values(),yt.values(),file3)
print
file3.write("\n")
file3.close()
#plt.plot(x,y, 'o')
#plt.plot(x, m*x + c, 'r', label='Fitted line')
#plt.show()
#for key,item in close.iteritems() :
#values[key] = [impo.get(key),bet.get(key),flow_b.get(key), load.get(key),com_c.get(key),com_b.get(key)]
return values
def centrality(net):
values = {}
close = nx.closeness_centrality(net, normalized=True)
eigen = nx.eigenvector_centrality_numpy(net)
page = nx.pagerank(net)
bet = nx.betweenness_centrality(net, normalized=True)
flow_c = nx.current_flow_closeness_centrality(net, normalized=True)
flow_b = nx.current_flow_betweenness_centrality(net, normalized=True)
load = nx.load_centrality(net, normalized=True)
com_c = nx.communicability_centrality(net)
com_b = nx.communicability_betweenness_centrality(net, normalized=True)
degree = net.degree()
file3 = open("bl.csv", 'w')
for xt in [
bet, load, degree, page, flow_b, com_c, com_b, eigen, close, flow_c
]: #[impo,bet,flow_b,load,com_c,com_b] :
for yt in [
bet, load, degree, page, flow_b, com_c, com_b, eigen, close,
flow_c
]: #[impo,bet,flow_b,load,com_c,com_b] :
corr(xt.values(), yt.values(), file3)
print
file3.write("\n")
file3.close()
#plt.plot(x,y, 'o')
#plt.plot(x, m*x + c, 'r', label='Fitted line')
#plt.show()
#for key,item in close.iteritems() :
#values[key] = [impo.get(key),bet.get(key),flow_b.get(key), load.get(key),com_c.get(key),com_b.get(key)]
return values
def centrality(G, measure, nodes_gdf, weight, normalized = False):
""""
The function computes several node centrality measures.
Parameters
----------
G: Networkx graph
measure: string
nodes_gdf: Point GeoDataFrame
nodes (junctions) GeoDataFrame
weight: string
edges weight
normalized: boolean
Returns
-------
dictionary
"""
if measure == "betweenness_centrality":
c = nx.betweenness_centrality(G, weight = weight, normalized=normalized)
elif measure == "straightness_centrality":
c = straightness_centrality(G, weight = weight, normalized=normalized)
elif measure == "closeness_centrality":
c = nx.closeness_centrality(G, weight = weight, normalized=normalized)
elif measure == "information_centrality":
c = nx.current_flow_betweenness_centrality(G, weight = weight, solver ="lu", normalized=normalized)
raise nameError("The name provided is not a valid centrality name associated with a function")
return c
def describe(G, ny_tri, chems):
global describeNetwork
'''
Describe the network: degrees, clustering, and centrality measures
'''
# Degree
# The number of connections a node has to other nodes.
degrees= nx.degree(G)
degrees_df = pd.DataFrame(degrees.items(), columns=['Facility', 'Degrees'])
values = sorted(set(degrees.values()))
hist = [degrees.values().count(x) for x in values]
plt.figure()
plt.plot(values, hist,'ro-') # degree
plt.xlabel('Degree')
plt.ylabel('Number of nodes')
plt.title('Degree Distribution')
plt.savefig('output/degree_distribution.png')
# Clustering coefficients
# The bipartie clustering coefficient is a measure of local density of connections.
clust_coefficients = nx.clustering(G)
clust_coefficients_df = pd.DataFrame(clust_coefficients.items(), columns=['Facility', 'Clustering Coefficient'])
clust_coefficients_df = clust_coefficients_df.sort('Clustering Coefficient', ascending=False)
#print clust_coefficients_df
# Node centrality measures
FCG=list(nx.connected_component_subgraphs(G, copy=True))[0]
# Current flow betweenness centrality
# Current-flow betweenness centrality uses an electrical current model for information spreading
# in contrast to betweenness centrality which uses shortest paths.
betweeness = nx.current_flow_betweenness_centrality(FCG)
betweeness_df = pd.DataFrame(betweeness.items(), columns=['Facility', 'Betweeness'])
betweeness_df = betweeness_df.sort('Betweeness', ascending=False)
# Closeness centrality
# The closeness of a node is the distance to all other nodes in the graph
# or in the case that the graph is not connected to all other nodes in the connected component containing that node.
closeness = nx.closeness_centrality(FCG)
closeness_df = pd.DataFrame(closeness.items(), columns=['Facility', 'Closeness'])
closeness_df = closeness_df.sort('Closeness', ascending=False)
# Eigenvector centrality
# Eigenvector centrality computes the centrality for a node based on the centrality of its neighbors.
# In other words, how connected a node is to other highly connected nodes.
eigenvector = nx.eigenvector_centrality(FCG)
eigenvector_df = pd.DataFrame(eigenvector.items(), columns=['Facility', 'Eigenvector'])
eigenvector_df = eigenvector_df.sort('Eigenvector', ascending=False)
# Create dataframe of facility info
fac_info = ny_tri[['tri_facility_id','facility_name', 'primary_naics', 'parent_company_name']].drop_duplicates()
fac_info.rename(columns={'facility_name':'Facility'}, inplace=True)
# Merge everything
describeNetwork = degrees_df.merge(
clust_coefficients_df,on='Facility').merge(
betweeness_df,on='Facility').merge(
closeness_df, on='Facility').merge(
eigenvector_df, on='Facility').merge(
fac_info, on='Facility', how='left').merge(
chems, on='Facility', how='left')
describeNetwork = describeNetwork.sort('Degrees', ascending=False)
describeNetwork.to_csv('output/describeNetwork.csv')
def test_P4(self):
"""Betweenness centrality: P4"""
G = nx.path_graph(4)
b = nx.current_flow_betweenness_centrality(G, normalized=False)
b_answer = {0: 0, 1: 2, 2: 2, 3: 0}
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
def test_star(self):
"""Betweenness centrality: star """
G=nx.Graph()
nx.add_star(G, ['a', 'b', 'c', 'd'])
b=nx.current_flow_betweenness_centrality(G,normalized=True)
b_answer={'a': 1.0, 'b': 0.0, 'c': 0.0, 'd':0.0}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_star(self):
"""Betweenness centrality: star"""
G = nx.Graph()
nx.add_star(G, ["a", "b", "c", "d"])
b = nx.current_flow_betweenness_centrality(G, normalized=True)
b_answer = {"a": 1.0, "b": 0.0, "c": 0.0, "d": 0.0}
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
def test_star(self):
"""Betweenness centrality: star """
G = nx.Graph()
nx.add_star(G, ["a", "b", "c", "d"])
b = nx.current_flow_betweenness_centrality_subset(G, list(G), list(G), normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_K4(self):
"Approximate current-flow betweenness centrality: K4"
G = nx.complete_graph(4)
b = nx.current_flow_betweenness_centrality(G, normalized=False)
epsilon = 0.1
ba = approximate_cfbc(G, normalized=False, epsilon=0.5 * epsilon)
for n in sorted(G):
np.testing.assert_allclose(b[n], ba[n], atol=epsilon * len(G)**2)
def test_star(self):
"""Betweenness centrality: star"""
G = nx.Graph()
nx.add_star(G, ["a", "b", "c", "d"])
b = nx.current_flow_betweenness_centrality(G, normalized=True)
b_answer = {"a": 1.0, "b": 0.0, "c": 0.0, "d": 0.0}
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
def test_K4_normalized(self):
"Approximate current-flow betweenness centrality: K4 normalized"
G=networkx.complete_graph(4)
b=networkx.current_flow_betweenness_centrality(G,normalized=True)
epsilon=0.1
ba = approximate_cfbc(G,normalized=True, epsilon=epsilon)
for n in sorted(G):
assert_allclose(b[n],ba[n],atol=epsilon)
def test_star(self):
"""Betweenness centrality: star """
G = networkx.Graph()
G.add_star(['a', 'b', 'c', 'd'])
b = networkx.current_flow_betweenness_centrality(G, normalized=True)
b_answer = {'a': 1.0, 'b': 0.0, 'c': 0.0, 'd': 0.0}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_grid(self):
"Approximate current-flow betweenness centrality: 2d grid"
G=nx.grid_2d_graph(4,4)
b=nx.current_flow_betweenness_centrality(G,normalized=True)
epsilon=0.1
ba = approximate_cfbc(G,normalized=True, epsilon=0.5*epsilon)
for n in sorted(G):
assert_allclose(b[n],ba[n],atol=epsilon)
def test_K4(self):
"Approximate current-flow betweenness centrality: K4"
G=nx.complete_graph(4)
b=nx.current_flow_betweenness_centrality(G,normalized=False)
epsilon=0.1
ba = approximate_cfbc(G,normalized=False, epsilon=0.5*epsilon)
for n in sorted(G):
assert_allclose(b[n],ba[n],atol=epsilon*len(G)**2)
def current_flow_betweenness(G):
"""Current-flow betweenness centrality"""
G = G.to_undirected()
G = invert_edge_weights(G)
if nx.is_connected(G):
return nx.current_flow_betweenness_centrality(G)
else:
return _aggregate_for_components(G, nx.current_flow_betweenness_centrality)
def test_K4_normalized(self):
"Approximate current-flow betweenness centrality: K4 normalized"
G = networkx.complete_graph(4)
b = networkx.current_flow_betweenness_centrality(G, normalized=True)
epsilon = 0.1
ba = approximate_cfbc(G, normalized=True, epsilon=0.5 * epsilon)
for n in sorted(G):
assert_allclose(b[n], ba[n], atol=epsilon)
def test_grid(self):
"Approximate current-flow betweenness centrality: 2d grid"
G = nx.grid_2d_graph(4, 4)
b = nx.current_flow_betweenness_centrality(G, normalized=True)
epsilon = 0.1
ba = approximate_cfbc(G, normalized=True, epsilon=0.5 * epsilon)
for n in sorted(G):
np.testing.assert_allclose(b[n], ba[n], atol=epsilon)
def test_star(self):
"Approximate current-flow betweenness centrality: star"
G=nx.Graph()
nx.add_star(G, ['a', 'b', 'c', 'd'])
b=nx.current_flow_betweenness_centrality(G,normalized=True)
epsilon=0.1
ba = approximate_cfbc(G,normalized=True, epsilon=0.5*epsilon)
for n in sorted(G):
assert_allclose(b[n],ba[n],atol=epsilon)
def test_solers(self):
"""Betweenness centrality: alternate solvers"""
G=nx.complete_graph(4)
for solver in ['full','lu','cg']:
b=nx.current_flow_betweenness_centrality(G,normalized=False,
solver=solver)
b_answer={0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_star(self):
"Approximate current-flow betweenness centrality: star"
G = nx.Graph()
nx.add_star(G, ["a", "b", "c", "d"])
b = nx.current_flow_betweenness_centrality(G, normalized=True)
epsilon = 0.1
ba = approximate_cfbc(G, normalized=True, epsilon=0.5 * epsilon)
for n in sorted(G):
np.testing.assert_allclose(b[n], ba[n], atol=epsilon)
def test_star(self):
"Approximate current-flow betweenness centrality: star"
G = networkx.Graph()
G.add_star(['a', 'b', 'c', 'd'])
b = networkx.current_flow_betweenness_centrality(G, normalized=True)
epsilon = 0.1
ba = approximate_cfbc(G, normalized=True, epsilon=0.5 * epsilon)
for n in sorted(G):
assert_allclose(b[n], ba[n], atol=epsilon)
def test_P4(self):
"""Betweenness centrality: P4"""
G = networkx.path_graph(4)
b = networkx.current_flow_betweenness_centrality_subset(
G, G.nodes(), G.nodes(), normalized=True)
b_answer = networkx.current_flow_betweenness_centrality(
G, normalized=True)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def current_flow_betweenness(G):
"""Current-flow betweenness centrality"""
G = G.to_undirected()
G = invert_edge_weights(G)
if nx.is_connected(G):
return nx.current_flow_betweenness_centrality(G)
else:
return _aggregate_for_components(
G, nx.current_flow_betweenness_centrality)
def test_solers(self):
"""Betweenness centrality: alternate solvers"""
G = nx.complete_graph(4)
for solver in ['full', 'lu', 'cg']:
b = nx.current_flow_betweenness_centrality(G, normalized=False,
solver=solver)
b_answer = {0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_P4(self):
"""Betweenness centrality: P4"""
G = nx.path_graph(4)
b = nx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
def test_K4_normalized(self):
"""Betweenness centrality: K4"""
G=nx.complete_graph(4)
b=nx.current_flow_betweenness_centrality(G,normalized=True)
b_answer={0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
G.add_edge(0,1,{'weight':0.5,'other':0.3})
b=nx.current_flow_betweenness_centrality(G,normalized=True,weight=None)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
wb_answer={0: 0.2222222, 1: 0.2222222, 2: 0.30555555, 3: 0.30555555}
b=nx.current_flow_betweenness_centrality(G,normalized=True)
for n in sorted(G):
assert_almost_equal(b[n],wb_answer[n])
wb_answer={0: 0.2051282, 1: 0.2051282, 2: 0.33974358, 3: 0.33974358}
b=nx.current_flow_betweenness_centrality(G,normalized=True,weight='other')
for n in sorted(G):
assert_almost_equal(b[n],wb_answer[n])
def test_solvers2(self):
"""Betweenness centrality: alternate solvers"""
G = nx.complete_graph(4)
for solver in ["full", "lu", "cg"]:
b = nx.current_flow_betweenness_centrality(
G, normalized=False, solver=solver
)
b_answer = {0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
def test_K4(self):
"""Betweenness centrality: K4"""
G = nx.complete_graph(4)
for solver in ["full", "lu", "cg"]:
b = nx.current_flow_betweenness_centrality(G,
normalized=False,
solver=solver)
b_answer = {0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
def test_P4_normalized(self):
"""Betweenness centrality: P4 normalized"""
G = nx.path_graph(4)
b = nx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
def test_K4_normalized(self):
"""Betweenness centrality: K4"""
G=networkx.complete_graph(4)
b=networkx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True)
b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def network_algorithms(g, dfs):
print("Calculating network algorithms")
# iterate over graph components
for i in dfs:
metrics = []
# find all edges of the subgraph and only keep the "offer" relationship
id_ = i.select("id").map(lambda a: a.id)
ids = id_.collect()
edges_ = g.edges.rdd.filter(lambda a: a.src in ids).toDF()
df = edges_.select("src", "dst").toPandas()
edge_list = [tuple(x) for x in df.values]
# generate a networkx graph
G = nx.Graph()
G.add_edges_from(edge_list)
# calculate several network metrics for the graph
metrics.append(result_to_pandas(dict(nx.degree(G)), "degree"))
metrics.append(result_to_pandas(nx.closeness_centrality(G), "closeness_centrality"))
metrics.append(result_to_pandas(nx.betweenness_centrality(G), "betweenness_centrality"))
metrics.append(result_to_pandas(nx.current_flow_closeness_centrality(G), "current_flow_closeness_centrality"))
metrics.append(result_to_pandas(nx.current_flow_betweenness_centrality(G), "current_flow_betweenness_centrality"))
metrics.append(result_to_pandas(nx.katz_centrality_numpy(G), "katz_centrality"))
metrics.append(result_to_pandas(nx.load_centrality(G), "load_centrality"))
metrics.append(result_to_pandas(nx.pagerank(G), "pagerank"))
# TypeError: Cannot use scipy.linalg.eig for sparse A with k >= N - 1. Use scipy.linalg.eig(A.toarray()) or reduce k.
# metrics.append(result_to_pandas(nx.eigenvector_centrality_numpy(G), "eigenvector_centrality"))
# join network metrics into one graph
res = pd.concat(metrics, axis=1, sort=False)
res = res.reset_index(drop=False)
res.rename(columns={"index": "id"}, inplace=True)
print(res)
# convert the result into spark dataframe
spark_df = sqlContext.createDataFrame(res)
# create or add to big dataframe that contains all components
try:
out = out.unionAll(spark_df)
except NameError:
out = spark_df
return out
def f30(self):
return "ND"
start = 0
try:
c_vals = nx.current_flow_betweenness_centrality(self.G).values()
res = sum(c_vals)
except nx.NetworkXError:
res = "ND"
stop = 0
# self.feature_time.append(stop - start)
return res
def test_star(self):
"""Betweenness centrality: star """
G=networkx.Graph()
G.add_star(['a','b','c','d'])
b=networkx.current_flow_betweenness_centrality_subset(G,
G.nodes(),
G.nodes(),
normalized=True)
b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_star(self):
"""Betweenness centrality: star """
G = nx.Graph()
nx.add_star(G, ['a', 'b', 'c', 'd'])
b = nx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert almost_equal(b[n], b_answer[n])
def f30(self):
return "ND"
start = 0
try:
c_vals = nx.current_flow_betweenness_centrality(self.G).values()
res = sum(c_vals)
except nx.NetworkXError:
res = "ND"
stop = 0
# self.feature_time.append(stop - start)
return res
def DegreeOfInterestMIPs(mip, user, obj, alpha=0.3, beta=0.7):
#compute apriori importance of node obj (considers effective conductance)
current_flow_betweeness = nx.current_flow_betweenness_centrality(mip, True, 'weight');
api_obj = current_flow_betweeness[obj] #node centrality
# print 'obj'
# print obj
# print 'api_obj'
# print api_obj
#compute proximity between user node and object node using Cycle-Free-Edge-Conductance from Koren et al. 2007
cfec_user_obj = CFEC(user,obj,mip)
# print 'cfec_user_obj'
# print cfec_user_obj
return alpha*api_obj+beta*cfec_user_obj
def DegreeOfInterestMIPs(self, user, obj, alpha=0.3, beta=0.7):
#compute apriori importance of node obj (considers effective conductance)
current_flow_betweeness = nx.current_flow_betweenness_centrality(True, 'weight');
api_obj = current_flow_betweeness[obj] #node centrality
# print 'obj'
# print obj
# print 'api_obj'
# print api_obj
#compute proximity between user node and object node using Cycle-Free-Edge-Conductance from Koren et al. 2007
cfec_user_obj = self.CFEC(user,obj)
# print 'cfec_user_obj'
# print cfec_user_obj
return alpha*api_obj+beta*cfec_user_obj #TODO: check that scales work out for centrality and proximity, otherwise need some normalization
def get_flow_betweenness(graph_data):
graph = graph_data['net']
w_label = graph_data['w_label']
# sem peso e sem direção
graph_simply = dir2undir(graph, w_label).simplify(
multiple=True, loops=False, combine_edges=graph_data['simply_method2'])
undir_unweig = networkx.current_flow_betweenness_centrality(
graph_simply.to_networkx(), weight=None)
# com peso
weig = networkx.current_flow_betweenness_centrality(
graph_simply.to_networkx(), weight=w_label)
# com direção
flow_in = networkx.current_flow_betweenness_centrality(graph.to_networkx(),
weight=None)
# com peso e com direção
flow_in_w = networkx.current_flow_betweenness_centrality(
graph.to_networkx(), weight=w_label)
return undir_unweig, weig, flow_in, flow_in_w
def test_K4(self):
"""Betweenness centrality: K4"""
G = nx.complete_graph(4)
b = nx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
# test weighted network
G.add_edge(0, 1, weight=0.5, other=0.3)
b = nx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True,
weight=None)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
b = nx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True)
b_answer = nx.current_flow_betweenness_centrality(G, normalized=True)
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
b = nx.current_flow_betweenness_centrality_subset(G,
list(G),
list(G),
normalized=True,
weight="other")
b_answer = nx.current_flow_betweenness_centrality(G,
normalized=True,
weight="other")
for n in sorted(G):
assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
def calculate_centrality(G):
# dc_dumps = json.dumps(nx.degree_centrality(G).items(),sort_keys=True,indent=4)
# dc_loads = json.loads(dc_dumps)
dc_sorted = sorted(nx.degree_centrality(G).items(), key=itemgetter(0), reverse=True)
bc_sorted = sorted(nx.betweenness_centrality(G).items(), key=itemgetter(0), reverse=True)
clc_sorted = sorted(nx.closeness_centrality(G).items(), key=itemgetter(0), reverse=True)
coc_sorted = sorted(nx.communicability_centrality(G).items(), key=itemgetter(0), reverse=True)
lc_sorted = sorted(nx.load_centrality(G).items(), key=itemgetter(0), reverse=True)
cfbc_sorted = sorted(nx.current_flow_betweenness_centrality(G).items(), key=itemgetter(0), reverse=True)
cfcc_sorted = sorted(nx.current_flow_closeness_centrality(G).items(), key=itemgetter(0), reverse=True)
# print ec_sorted[0]
developer_centrality = []
developer_file = file("public/wordpress/developer.json")
developers = json.load(developer_file)
for developer in developers:
degree = 0
betweenness = 0
closeness = 0
communicability = 0
load = 0
current_flow_betweenness = 0
current_flow_closeness = 0
for i in range (0, len(dc_sorted)):
# if ( not dc_sorted[i][0] == bc_sorted[i][0] == clc_sorted[i][0] == coc_sorted[i][0] == lc_sorted[i][0] == cfbc_sorted[i][0]):
# print 'false'
if( developer['developer'] == dc_sorted[i][0]):
degree = dc_sorted[i][1]
betweenness = bc_sorted[i][1]
closeness = clc_sorted[i][1]
communicability = coc_sorted[i][1]
load = lc_sorted[i][1]
current_flow_betweenness = cfbc_sorted[i][1]
current_flow_closeness = cfcc_sorted[i][1]
developer_centrality.append({
'name': developer['developer'],
'degree': degree,
'betweenness': betweenness,
'closeness': closeness,
'communicability': communicability,
'load': load,
'current_flow_betweenness': current_flow_betweenness,
'current_flow_closeness':current_flow_closeness,
})
return developer_centrality
def weightGraph(self, datacontacts, mi_threshold, time_treshold=0.6):
if len(self.mol.get('resid', 'name CA')) != len(self.resids):
raise Exception('The length of the protein doesn\'t match the Mutual Information data')
contactcat = np.concatenate(datacontacts.dat)
contacts_matrix = np.zeros([len(self.resids), len(self.resids)])
for i in range(contactcat.shape[1]):
counter = np.count_nonzero(contactcat[:, i])
resid1 = self.residmap[self.mol.resid[datacontacts.description.atomIndexes[i][0]]]
resid2 = self.residmap[self.mol.resid[datacontacts.description.atomIndexes[i][1]]]
contacts_matrix[resid1][resid2] = counter
self.graph_array = np.zeros([contacts_matrix.shape[0], contacts_matrix.shape[0]])
mask = (self.mi_matrix > mi_threshold) & (contacts_matrix > (time_treshold * contactcat.shape[0]))
self.graph_array[mask] = self.mi_matrix[mask]
intermed = []
for source in range(self.graph_array.shape[0]):
for target in range(source, self.graph_array.shape[1]):
if self.graph_array[source, target] != 0 and target > source:
intermed.append(
[int(self.resids[source]), int(self.resids[target]), float(self.graph_array[source, target])])
import pandas as pd
import networkx as nx
from sklearn.cluster.spectral import SpectralClustering
pd = pd.DataFrame(intermed, columns=['source', 'target', 'weight'])
pd[['source', 'target']] = pd[['source', 'target']].astype(type('int', (int,), {}))
pd['weight'] = pd['weight'].astype(type('float', (float,), {}))
G = nx.from_pandas_dataframe(pd, 'source', 'target', ['weight'])
## setSegment
segids = self.mol.get('segid', 'name CA')
seg_res_dict = {key: value for (key, value) in zip(self.resids, segids) if
np.any(pd.loc[(pd['source'] == key)].index) or np.any(pd.loc[(pd['target'] == key)].index)}
nx.set_node_attributes(G, 'Segment', seg_res_dict)
## set
if not nx.is_connected(G):
G = max(nx.connected_component_subgraphs(G), key=len)
flow_cent = nx.current_flow_betweenness_centrality(G, weight='weight')
nx.set_node_attributes(G, 'flowcent', flow_cent)
Spectre = SpectralClustering(n_clusters=10, affinity='precomputed')
model = Spectre.fit_predict(self.graph_array)
model = model.astype(type('float', (float,), {}))
spectral_dict = {key: value for (key, value) in zip(self.resids, model) if key in G.nodes()}
nx.set_node_attributes(G, 'spectral', spectral_dict)
self.graph = G
def compute_metrics(G, w='weight'):
for e in G.edges():
G[e[0]][e[1]][w] = float(G[e[0]][e[1]][w])
pr = nx.pagerank(G,weight=w, tol=0.000001)
nx.set_node_attributes(G,'pagerank',pr)
eig = nx.eigenvector_centrality_numpy(G)
nx.set_node_attributes(G, 'eigencent',eig)
ug = _di_to_undi(G)
cf = nx.current_flow_betweenness_centrality(ug,weight=w)
cf2 = dict.fromkeys(cf)
for k in cf2.keys():
cf2[k] = float(cf[k])
nx.set_node_attributes(G,'currentflow',cf2)
deg = G.degree()
nx.set_node_attributes(G,'degree',deg)
stren = G.degree(weight=w)
nx.set_node_attributes(G,'strength',stren)
def initializeMIP(self):
self.pars.append({})
userId = self.addUser(self.currentVersion.author)
session = Session(self.currentVersion.author, self.currentVersion, len(self.log))
partext = [a.text.encode('utf-8') for a in self.currentVersion.paragraphs]
# print userId
index = 0
for par in self.currentVersion.paragraphs:
parId = self.addPars(None, index)
# print parId
self.updateEdge(userId, parId,'u-p', self.sigIncrement)
session.actions.append(Action(session.user, parId, 'sigEdit',partext[index], self.sigIncrement,1))
index=index+1
for i in range(0,len(self.currentVersion.paragraphs)):
for j in range(i+1,len(self.currentVersion.paragraphs)):
if i!=j:
self.updateEdge(self.pars[self.latestVersion-1][i], self.pars[self.latestVersion][j],'p-p', self.sigIncrement)
self.log.append(session)
self.current_flow_betweeness = nx.current_flow_betweenness_centrality(self.mip,True, 'weight')
print len(self.log)
def add_current_flow_betweenness_node(graf):
print "Adding current flow BETWEENNESS to nodes"
cfb_dict = nx.current_flow_betweenness_centrality(graf)
nx.set_node_attributes(graf, 'cfb', cfb_dict)
stats = dict2column(stats, wdegree, 'wdegree')
#Current Closeness Centrality
flowcloseness = nx.current_flow_closeness_centrality(mg, weight = 'exmptgross')
stats = dict2column(stats, flowcloseness, 'flowcloseness')
#Vertex Closeness
closeness = nx.closeness_centrality(mg)
stats = dict2column(stats, closeness, 'closeness')
#Vertex Betweenness
btwnness = nx.betweenness_centrality(mg)
stats = dict2column(stats, btwnness, 'btwnness')
#Current Betweenness
flowbtwnness = nx.current_flow_betweenness_centrality(mg, weight = 'exmptgross')
stats = dict2column(stats, flowbtwnness, 'flowbtwnness')
#Eigenvector Centrality
eigenvc = nx.eigenvector_centrality(mg)
stats = dict2column(stats, eigenvc, 'eigenvc')
#Weighted Eigenvector Centrality
weigenvc = we.eigenvector_centrality(mg, weight = 'exmptgross')
stats = dict2column(stats, weigenvc, 'weigenvc')
stats['shortname'] = stats['MSAName'].apply(lambda x: re.search('^.*?[-,]',x).group(0)[:-1] + re.search(', [A-Z][A-Z]',x).group(0))
#Try drawing
direct = C.get_edge_data(source, target)['resistance']
endpoints[source] = endpoints.get(source,0) + 1
endpoints[target] = endpoints.get(target,0) + 1
with open('output/%s.csv' % path_code, 'wb') as f:
f.write("Endpoint, Paths using %s as bridge\n" % code_to_name[path_code])
for k,v in endpoints.iteritems():
f.write("%s, %d\n" % (code_to_name[k], v))
# Find Centrality
print("Calculating shortest-path betweenness centrality for country graph")
results = nx.betweenness_centrality(C, weight="resistance")
nx.set_node_attributes(C, 'betweenness_centrality', results)
print("Calculating random-walk betweenness centrality for country graph")
results = nx.current_flow_betweenness_centrality(C, weight="weight")
nx.set_node_attributes(C, 'random_walk_betweenness_centrality', results)
#print("Calculating eigenvector centrality for country graph")
#results = nx.eigenvector_centrality(C, weight="weight")
#nx.set_node_attributes(C, 'eigenvector_centrality', results)
with open('output/countries.csv', 'wb') as f:
f.write("Code, Name, Shortest Path Betweenness, Random Walk Betweenness\n")
for node in sorted(C.nodes(data=True), key=lambda k: k[1]['betweenness_centrality'], reverse=True):
f.write("%s, %s, %f, %f\n" % (node[0], node[1]['name'], node[1]['betweenness_centrality'], node[1]['random_walk_betweenness_centrality']))
# Create bipartite graph
print("Creating bipartite country-video graph")
G = graph.BiGraph()
for loc in locs.countries():
name = loc.code
if loc.code == '--':
def flow_between(net):
return distriCentra(nx.current_flow_betweenness_centrality(net,normalized= True).values(),
nx.current_flow_betweenness_centrality(star(net),normalized= True).values(),
'current_flow_betweenness')
