def test_decomposed_local_centrality():
# centralities on the original nodes within the decomposed network should equal non-decomposed workflow
betas = np.array([-0.02, -0.01, -0.005, -0.0008, -0.0])
distances = networks.distance_from_beta(betas)
measure_keys = ('node_density',
'node_farness',
'node_cycles',
'node_harmonic',
'node_beta',
'segment_density',
'segment_harmonic',
'segment_beta',
'node_betweenness',
'node_betweenness_beta',
'segment_betweenness')
# test a decomposed graph
G = mock.mock_graph()
G = graphs.nX_simple_geoms(G)
node_uids, node_data, edge_data, node_edge_map = graphs.graph_maps_from_nX(G) # generate node and edge maps
measures_data = centrality.local_centrality(node_data,
edge_data,
node_edge_map,
distances,
betas,
measure_keys,
angular=False)
G_decomposed = graphs.nX_decompose(G, 20)
# generate node and edge maps
node_uids, node_data, edge_data, node_edge_map = graphs.graph_maps_from_nX(G_decomposed)
checks.check_network_maps(node_data, edge_data, node_edge_map)
measures_data_decomposed = centrality.local_centrality(node_data,
edge_data,
node_edge_map,
distances,
betas,
measure_keys,
angular=False)
# test harmonic closeness on original nodes for non-decomposed vs decomposed
d_range = len(distances)
m_range = len(measure_keys)
assert measures_data.shape == (m_range, d_range, len(G))
assert measures_data_decomposed.shape == (m_range, d_range, len(G_decomposed))
original_node_idx = np.where(node_data[:, 3] == 0)
# with increasing decomposition:
# - node based measures will not match
# - node based segment measures will match - these measure to the cut endpoints per thresholds
# - betweenness based segment won't match - doesn't measure to cut endpoints
for m_idx in range(m_range):
print(m_idx)
for d_idx in range(d_range):
match = np.allclose(measures_data[m_idx][d_idx], measures_data_decomposed[m_idx][d_idx][original_node_idx],
atol=0.1, rtol=0) # relax precision
if not match:
print('key', measure_keys[m_idx], 'dist:', distances[d_idx], 'match:', match)
if m_idx in [5, 6, 7]:
assert match
def __init__(self,
networkX_graph: nx.Graph,
distances: Union[list, tuple, np.ndarray] = None,
betas: Union[list, tuple, np.ndarray] = None,
min_threshold_wt: float = checks.def_min_thresh_wt):
node_uids, node_data, edge_data, node_edge_map = graphs.graph_maps_from_nX(
networkX_graph)
super().__init__(node_uids, node_data, edge_data, node_edge_map,
distances, betas, min_threshold_wt)
# keep reference to networkX graph
self.networkX = networkX_graph
def test_local_centrality_time():
'''
originally based on node_harmonic and node_betweenness:
OLD VERSION with trim maps:
Timing: 10.490865555 for 10000 iterations
NEW VERSION with numba typed list - faster and removes arcane full vs. trim maps workflow
8.242256040000001 for 10000 iterations
VERSION with node_edge_map Dict - tad slower but worthwhile for cleaner and more intuitive code
8.882408618 for 10000 iterations
float64 - 17.881911942000002
float32 - 13.612861239
segments of unreachable code add to timing regardless...
possibly because of high number of iters vs. function prep and teardown...?
14.4 -> 14.293403884 for simpler ghost node workflow
'''
# load the test graph
G = mock.mock_graph()
G = graphs.nX_simple_geoms(G)
node_uids, node_data, edge_data, node_edge_map = graphs.graph_maps_from_nX(G) # generate node and edge maps
# needs a large enough beta so that distance thresholds aren't encountered
distances = np.array([np.inf])
betas = networks.beta_from_distance(distances)
# setup timing wrapper
def wrapper_func():
'''
node density invokes aggregative workflow
betweenness node invokes betweenness workflow
segment density invokes segments workflow
'''
return centrality.local_centrality(node_data,
edge_data,
node_edge_map,
distances,
betas,
('node_density', # 7.16s
'node_betweenness', # 8.08s - adds around 1s
'segment_density', # 11.2s - adds around 3s
'segment_betweenness'
),
angular=False,
suppress_progress=True)
# prime the function
wrapper_func()
iters = 10000
# time and report
func_time = timeit.timeit(wrapper_func, number=iters)
print(f'Timing: {func_time} for {iters} iterations')
if 'GITHUB_ACTIONS' not in os.environ:
assert func_time < 20
def test_Network_Layer():
G = mock.mock_graph()
G = graphs.nX_simple_geoms(G)
# manual graph maps for comparison
node_uids, node_data, edge_data, node_edge_map = graphs.graph_maps_from_nX(
G)
x_arr = node_data[:, 0]
y_arr = node_data[:, 1]
betas = [-0.02, -0.005]
distances = networks.distance_from_beta(betas)
# test Network_Layer's class
for d, b in zip([distances, None], [None, betas]):
for angular in [True, False]:
N = networks.Network_Layer(node_uids,
node_data,
edge_data,
node_edge_map,
distances=d,
betas=b)
assert np.allclose(N.uids, node_uids, atol=0.001, rtol=0)
assert np.allclose(N._node_data, node_data, atol=0.001, rtol=0)
assert np.allclose(N._edge_data, edge_data, atol=0.001, rtol=0)
assert np.allclose(
N.distances, distances, atol=0.001,
rtol=0) # inferred automatically when only betas provided
assert np.allclose(
N.betas, betas, atol=0.001,
rtol=0) # inferred automatically when only distances provided
assert N.min_threshold_wt == checks.def_min_thresh_wt
assert np.allclose(N.x_arr, x_arr, atol=0.001, rtol=0)
assert np.allclose(N.y_arr, y_arr, atol=0.001, rtol=0)
assert np.allclose(N.live, node_data[:, 2], atol=0.001, rtol=0)
assert np.allclose(N.ghosted, node_data[:, 3], atol=0.001, rtol=0)
assert np.allclose(N.edge_lengths,
edge_data[:, 2],
atol=0.001,
rtol=0)
assert np.allclose(N.edge_angles,
edge_data[:, 3],
atol=0.001,
rtol=0)
assert np.allclose(N.edge_impedance_factor,
edge_data[:, 4],
atol=0.001,
rtol=0)
assert np.allclose(N.edge_in_bearing,
edge_data[:, 5],
atol=0.001,
rtol=0)
assert np.allclose(N.edge_out_bearing,
edge_data[:, 6],
atol=0.001,
rtol=0)
# test round-trip graph to and from Network_Layer
N = networks.Network_Layer(node_uids,
node_data,
edge_data,
node_edge_map,
distances=distances)
G_round_trip = N.to_networkX()
# graph_maps_from_networkX generates implicit live (all True) and weight (all 1) attributes if missing
# i.e. can't simply check that all nodes equal, so check properties manually
for n, d in G.nodes(data=True):
assert n in G_round_trip
assert G_round_trip.nodes[n]['x'] == d['x']
assert G_round_trip.nodes[n]['y'] == d['y']
# edges can be checked en masse
assert G_round_trip.edges == G.edges
# check alternate min_threshold_wt gets passed through successfully
alt_min = 0.02
alt_distances = networks.distance_from_beta(betas,
min_threshold_wt=alt_min)
N = networks.Network_Layer(node_uids,
node_data,
edge_data,
node_edge_map,
betas=betas,
min_threshold_wt=alt_min)
assert np.allclose(N.distances, alt_distances, atol=0.001, rtol=0)
# check for malformed signatures
with pytest.raises(ValueError):
networks.Network_Layer(node_uids[:-1], node_data, edge_data,
node_edge_map, distances)
with pytest.raises(ValueError):
networks.Network_Layer(node_uids, node_data[:, :-1], edge_data,
node_edge_map, distances)
with pytest.raises(ValueError):
networks.Network_Layer(node_uids, node_data, edge_data[:, :-1],
node_edge_map, distances)
with pytest.raises(ValueError):
networks.Network_Layer(node_uids, node_data, edge_data[:, :-1],
node_edge_map, distances)
with pytest.raises(ValueError):
networks.Network_Layer(node_uids, node_data, edge_data,
node_edge_map) # no betas or distances
with pytest.raises(ValueError):
networks.Network_Layer(node_uids,
node_data,
edge_data,
node_edge_map,
distances=None,
betas=None)
with pytest.raises(ValueError):
networks.Network_Layer(node_uids,
node_data,
edge_data,
node_edge_map,
distances=[])
with pytest.raises(ValueError):
networks.Network_Layer(node_uids,
node_data,
edge_data,
node_edge_map,
betas=[])
def test_Network_Layer_From_nX():
G = mock.mock_graph()
G = graphs.nX_simple_geoms(G)
node_uids, node_data, edge_data, node_edge_map = graphs.graph_maps_from_nX(
G)
x_arr = node_data[:, 0]
y_arr = node_data[:, 1]
betas = np.array([-0.04, -0.02])
distances = networks.distance_from_beta(betas)
# test Network_Layer_From_NetworkX's class
for d, b in zip([distances, None], [None, betas]):
for angular in [True, False]:
N = networks.Network_Layer_From_nX(G, distances=d, betas=b)
assert np.allclose(N.uids, node_uids, atol=0.001, rtol=0)
assert np.allclose(N._node_data, node_data, atol=0.001, rtol=0)
assert np.allclose(N._edge_data, edge_data, atol=0.001, rtol=0)
assert np.allclose(
N.distances, distances, atol=0.001,
rtol=0) # inferred automatically when only betas provided
assert np.allclose(
N.betas, betas, atol=0.001,
rtol=0) # inferred automatically when only distances provided
assert N.min_threshold_wt == checks.def_min_thresh_wt
assert np.allclose(N.x_arr, x_arr, atol=0.001, rtol=0)
assert np.allclose(N.y_arr, y_arr, atol=0.001, rtol=0)
assert np.allclose(N.live, node_data[:, 2], atol=0.001, rtol=0)
assert np.allclose(N.edge_lengths,
edge_data[:, 2],
atol=0.001,
rtol=0)
assert np.allclose(N.edge_angles,
edge_data[:, 3],
atol=0.001,
rtol=0)
assert np.allclose(N.edge_impedance_factor,
edge_data[:, 4],
atol=0.001,
rtol=0)
assert np.allclose(N.edge_in_bearing,
edge_data[:, 5],
atol=0.001,
rtol=0)
assert np.allclose(N.edge_out_bearing,
edge_data[:, 6],
atol=0.001,
rtol=0)
# check alternate min_threshold_wt gets passed through successfully
alt_min = 0.02
alt_distances = networks.distance_from_beta(betas,
min_threshold_wt=alt_min)
N = networks.Network_Layer_From_nX(G,
betas=betas,
min_threshold_wt=alt_min)
assert np.allclose(N.distances, alt_distances, atol=0.001, rtol=0)
# check for malformed signatures
with pytest.raises(TypeError):
networks.Network_Layer_From_nX('boo', distances=distances)
with pytest.raises(ValueError):
networks.Network_Layer_From_nX(G) # no betas or distances
with pytest.raises(ValueError):
networks.Network_Layer_From_nX(G, distances=None, betas=None)
with pytest.raises(ValueError):
networks.Network_Layer_From_nX(G, distances=[])
with pytest.raises(ValueError):
networks.Network_Layer_From_nX(G, betas=[])
def test_nX_from_graph_maps():
# also see test_networks.test_to_networkX for tests on implementation via Network layer
# check round trip to and from graph maps results in same graph
G = mock.mock_graph()
G = graphs.nX_simple_geoms(G)
# explicitly set live params for equality checks
# graph_maps_from_networkX generates these implicitly if missing
for n in G.nodes():
G.nodes[n]['live'] = bool(np.random.randint(0, 1))
# test directly from and to graph maps
node_uids, node_data, edge_data, node_edge_map = graphs.graph_maps_from_nX(G)
G_round_trip = graphs.nX_from_graph_maps(node_uids, node_data, edge_data, node_edge_map)
assert list(G_round_trip.nodes) == list(G.nodes)
assert list(G_round_trip.edges) == list(G.edges)
# check with metrics dictionary
N = networks.Network_Layer_From_nX(G, distances=[500, 1000])
N.compute_centrality(measures=['node_harmonic'])
data_dict = mock.mock_data_dict(G)
landuse_labels = mock.mock_categorical_data(len(data_dict))
D = layers.Data_Layer_From_Dict(data_dict)
D.assign_to_network(N, max_dist=400)
D.compute_aggregated(landuse_labels,
mixed_use_keys=['hill', 'shannon'],
accessibility_keys=['a', 'c'],
qs=[0, 1])
metrics_dict = N.metrics_to_dict()
# without backbone
G_round_trip_data = graphs.nX_from_graph_maps(node_uids,
node_data,
edge_data,
node_edge_map,
metrics_dict=metrics_dict)
for uid, metrics in metrics_dict.items():
assert G_round_trip_data.nodes[uid]['metrics'] == metrics
# with backbone
G_round_trip_data = graphs.nX_from_graph_maps(node_uids,
node_data,
edge_data,
node_edge_map,
networkX_graph=G,
metrics_dict=metrics_dict)
for uid, metrics in metrics_dict.items():
assert G_round_trip_data.nodes[uid]['metrics'] == metrics
# test with decomposed
G_decomposed = graphs.nX_decompose(G, decompose_max=20)
# set live explicitly
for n in G_decomposed.nodes():
G_decomposed.nodes[n]['live'] = bool(np.random.randint(0, 1))
node_uids_d, node_data_d, edge_data_d, node_edge_map_d = graphs.graph_maps_from_nX(G_decomposed)
G_round_trip_d = graphs.nX_from_graph_maps(node_uids_d, node_data_d, edge_data_d, node_edge_map_d)
assert list(G_round_trip_d.nodes) == list(G_decomposed.nodes)
for n, node_data in G_round_trip.nodes(data=True):
assert n in G_decomposed
assert node_data['live'] == G_decomposed.nodes[n]['live']
assert node_data['x'] == G_decomposed.nodes[n]['x']
assert node_data['y'] == G_decomposed.nodes[n]['y']
assert G_round_trip_d.edges == G_decomposed.edges
# error checks for when using backbone graph:
# mismatching numbers of nodes
corrupt_G = G.copy()
corrupt_G.remove_node(0)
with pytest.raises(ValueError):
graphs.nX_from_graph_maps(node_uids, node_data, edge_data, node_edge_map, networkX_graph=corrupt_G)
# mismatching node uid
with pytest.raises(ValueError):
corrupt_node_uids = list(node_uids)
corrupt_node_uids[0] = 'boo'
graphs.nX_from_graph_maps(corrupt_node_uids, node_data, edge_data, node_edge_map, networkX_graph=G)
def test_graph_maps_from_nX():
# template graph
G_template = mock.mock_graph()
G_template = graphs.nX_simple_geoms(G_template)
# test maps vs. networkX
G_test = G_template.copy()
# set some random 'live' statuses
for n in G_test.nodes():
G_test.nodes[n]['live'] = bool(np.random.randint(0, 1))
# randomise the imp_factors
for s, e in G_test.edges():
G_test[s][e]['imp_factor'] = np.random.random() * 2
# generate geom with angular change for edge 50-51 - should sum to 360
angle_geom = geometry.LineString([
[700700, 5719900],
[700700, 5720000],
[700750, 5720050],
[700700, 5720050],
[700700, 5720100]
])
G_test[50][51]['geom'] = angle_geom
# generate test maps
node_uids, node_data, edge_data, node_edge_map = graphs.graph_maps_from_nX(G_test)
# debug plot
# plot.plot_graphs(primal=G_test)
# plot.plot_graph_maps(node_uids, node_data, edge_data)
# run check
checks.check_network_maps(node_data, edge_data, node_edge_map)
# check lengths
assert len(node_uids) == len(node_data) == G_test.number_of_nodes()
# no ghosted edges, so edges = x2
assert len(edge_data) == G_test.number_of_edges() * 2
# check node maps (idx and label match in this case...)
for n_label in node_uids:
assert node_data[n_label][0] == G_test.nodes[n_label]['x']
assert node_data[n_label][1] == G_test.nodes[n_label]['y']
assert node_data[n_label][2] == G_test.nodes[n_label]['live']
assert node_data[n_label][3] == 0 # ghosted is False by default
# check edge maps (idx and label match in this case...)
for start, end, length, angle_sum, imp_factor, start_bearing, end_bearing in edge_data:
assert np.allclose(length, G_test[start][end]['geom'].length, atol=0.001, rtol=0)
if (start == 50 and end == 51) or (start == 51 and end == 50):
# check that the angle is measured along the line of change
# i.e. 45 + 135 + 90 (not 45 + 45 + 90)
# angles are transformed per: 1 + (angle_sum / 180)
assert angle_sum == 270
else:
assert angle_sum == 0
assert np.allclose(imp_factor, G_test[start][end]['imp_factor'], atol=0.001, rtol=0)
s_x, s_y = node_data[int(start)][:2]
e_x, e_y = node_data[int(end)][:2]
assert np.allclose(start_bearing, np.rad2deg(np.arctan2(e_y - s_y, e_x - s_x)), atol=0.001, rtol=0)
assert np.allclose(end_bearing, np.rad2deg(np.arctan2(e_y - s_y, e_x - s_x)), atol=0.001, rtol=0)
# check that missing geoms throw an error
G_test = G_template.copy()
for s, e in G_test.edges():
# delete key from first node and break
del G_test[s][e]['geom']
break
with pytest.raises(KeyError):
graphs.graph_maps_from_nX(G_test)
# check that non-LineString geoms throw an error
G_test = G_template.copy()
for s, e in G_test.edges():
G_test[s][e]['geom'] = geometry.Point([G_test.nodes[s]['x'], G_test.nodes[s]['y']])
with pytest.raises(TypeError):
graphs.graph_maps_from_nX(G_test)
# check that missing node keys throw an error
G_test = G_template.copy()
for k in ['x', 'y']:
for n in G_test.nodes():
# delete key from first node and break
del G_test.nodes[n][k]
break
with pytest.raises(KeyError):
graphs.graph_maps_from_nX(G_test)
# check that invalid imp_factors are caught
G_test = G_template.copy()
# corrupt imp_factor value and break
for corrupt_val in [-1, -np.inf, np.nan]:
for s, e in G_test.edges():
G_test[s][e]['imp_factor'] = corrupt_val
break
with pytest.raises(ValueError):
graphs.graph_maps_from_nX(G_test)
def test_shortest_path_tree():
# prepare primal graph
G_primal = mock.mock_graph()
G_primal = graphs.nX_simple_geoms(G_primal)
node_uids_p, node_data_p, edge_data_p, node_edge_map_p = graphs.graph_maps_from_nX(G_primal)
# prepare round-trip graph for checks
G_round_trip = graphs.nX_from_graph_maps(node_uids_p, node_data_p, edge_data_p, node_edge_map_p)
# prepare dual graph
G_dual = mock.mock_graph()
G_dual = graphs.nX_simple_geoms(G_dual)
G_dual = graphs.nX_to_dual(G_dual)
node_uids_d, node_data_d, edge_data_d, node_edge_map_d = graphs.graph_maps_from_nX(G_dual)
assert len(node_uids_d) > len(node_uids_p)
# test all shortest paths against networkX version of dijkstra
for max_dist in [0, 500, 2000, np.inf]:
for src_idx in range(len(G_primal)):
# check shortest path maps
tree_map, tree_edges = centrality.shortest_path_tree(edge_data_p,
node_edge_map_p,
src_idx,
max_dist=max_dist,
angular=False)
tree_preds_p = tree_map[:, 1]
tree_dists_p = tree_map[:, 2]
tree_imps_p = tree_map[:, 3]
# compare against networkx dijkstra
nx_dist, nx_path = nx.single_source_dijkstra(G_round_trip, src_idx, weight='length', cutoff=max_dist)
for j in range(len(G_primal)):
if j in nx_path:
assert _find_path(j, src_idx, tree_preds_p) == nx_path[j]
assert np.allclose(tree_imps_p[j], tree_dists_p[j], atol=0.001, rtol=0)
assert np.allclose(tree_imps_p[j], nx_dist[j], atol=0.001, rtol=0)
# compare angular impedances and paths for a selection of targets on primal vs. dual
# this works for this graph because edge segments are straight
p_source_idx = node_uids_p.index(0)
primal_targets = (15, 20, 37)
dual_sources = ('0_1', '0_16', '0_31')
dual_targets = ('13_15', '17_20', '36_37')
for p_target, d_source, d_target in zip(primal_targets, dual_sources, dual_targets):
p_target_idx = node_uids_p.index(p_target)
d_source_idx = node_uids_d.index(d_source) # dual source index changes depending on direction
d_target_idx = node_uids_d.index(d_target)
tree_map_p, tree_edges_p = centrality.shortest_path_tree(edge_data_p,
node_edge_map_p,
p_source_idx,
max_dist=max_dist,
angular=True)
tree_imps_p = tree_map_p[:, 3]
tree_map_d, tree_edges_d = centrality.shortest_path_tree(edge_data_d,
node_edge_map_d,
d_source_idx,
max_dist=max_dist,
angular=True)
tree_imps_d = tree_map_d[:, 3]
assert np.allclose(tree_imps_p[p_target_idx], tree_imps_d[d_target_idx], atol=0.001, rtol=0)
# angular impedance should take a simpler but longer path - test basic case on dual
# for debugging
# from cityseer.util import plot
# plot.plot_nX_primal_or_dual(primal=G_primal, dual=G_dual, labels=True)
# source and target are the same for either
src_idx = node_uids_d.index('11_6')
target = node_uids_d.index('39_40')
# SIMPLEST PATH: get simplest path tree using angular impedance
tree_map, tree_edges = centrality.shortest_path_tree(edge_data_d,
node_edge_map_d,
src_idx,
max_dist=np.inf,
angular=True)
# find path
tree_preds = tree_map[:, 1]
path = _find_path(target, src_idx, tree_preds)
path_transpose = [node_uids_d[n] for n in path]
# takes 1597m route via long outside segment
# tree_dists[int(full_to_trim_idx_map[node_labels.index('39_40')])]
assert path_transpose == ['11_6', '11_14', '10_14', '10_43', '43_44', '40_44', '39_40']
# SHORTEST PATH:
# get shortest path tree using non angular impedance
tree_map, tree_edges = centrality.shortest_path_tree(edge_data_d,
node_edge_map_d,
src_idx,
max_dist=np.inf,
angular=False)
# find path
tree_preds = tree_map[:, 1]
path = _find_path(target, src_idx, tree_preds)
path_transpose = [node_uids_d[n] for n in path]
# takes 1345m shorter route
# tree_dists[int(full_to_trim_idx_map[node_labels.index('39_40')])]
assert path_transpose == ['11_6', '6_7', '3_7', '3_4', '1_4', '0_1', '0_31', '31_32', '32_34', '34_37', '37_39',
'39_40']
# NO SIDESTEPS - explicit check that sidesteps are prevented
src_idx = node_uids_d.index('10_43')
target = node_uids_d.index('10_5')
tree_map, tree_edges = centrality.shortest_path_tree(edge_data_d,
node_edge_map_d,
src_idx,
max_dist=np.inf,
angular=True)
# find path
tree_preds = tree_map[:, 1]
path = _find_path(target, src_idx, tree_preds)
path_transpose = [node_uids_d[n] for n in path]
assert path_transpose == ['10_43', '10_5']
# WITH SIDESTEPS - set angular flag to False
# manually overwrite distance impedances with angular for this test
# (angular has to be false otherwise shortest-path sidestepping avoided)
edge_data_d_temp = edge_data_d.copy()
# angular impedances at index 3 copied to distance impedances at distance 2
edge_data_d_temp[:, 2] = edge_data_d_temp[:, 3]
tree_map, tree_edges = centrality.shortest_path_tree(edge_data_d_temp,
node_edge_map_d,
src_idx,
max_dist=np.inf,
angular=False)
# find path
tree_preds = tree_map[:, 1]
path = _find_path(target, src_idx, tree_preds)
path_transpose = [node_uids_d[n] for n in path]
assert path_transpose == ['10_43', '10_14', '10_5']
def test_local_centrality():
'''
Also tested indirectly via test_networks.test_compute_centrality
Test centrality methods where possible against NetworkX - i.e. harmonic closeness and betweenness
Note that NetworkX improved closeness is not the same as derivation used in this package
NetworkX doesn't have a maximum distance cutoff, so run on the whole graph (low beta / high distance)
'''
# load the test graph
G = mock.mock_graph()
G = graphs.nX_simple_geoms(G)
node_uids, node_data, edge_data, node_edge_map = graphs.graph_maps_from_nX(G) # generate node and edge maps
G_round_trip = graphs.nX_from_graph_maps(node_uids, node_data, edge_data, node_edge_map)
# plots for debugging
# needs a large enough beta so that distance thresholds aren't encountered
betas = np.array([-0.02, -0.01, -0.005, -0.0008, -0.0])
distances = networks.distance_from_beta(betas)
# set the keys - add shuffling to be sure various orders work
measure_keys = [
'node_density',
'node_farness',
'node_cycles',
'node_harmonic',
'node_beta',
'segment_density',
'segment_harmonic',
'segment_beta',
'node_betweenness',
'node_betweenness_beta',
'segment_betweenness'
]
np.random.shuffle(measure_keys) # in place
measure_keys = tuple(measure_keys)
# generate the measures
measures_data = centrality.local_centrality(node_data,
edge_data,
node_edge_map,
distances,
betas,
measure_keys,
angular=False)
node_density = measures_data[measure_keys.index('node_density')]
node_farness = measures_data[measure_keys.index('node_farness')]
node_cycles = measures_data[measure_keys.index('node_cycles')]
node_harmonic = measures_data[measure_keys.index('node_harmonic')]
node_beta = measures_data[measure_keys.index('node_beta')]
segment_density = measures_data[measure_keys.index('segment_density')]
segment_harmonic = measures_data[measure_keys.index('segment_harmonic')]
segment_beta = measures_data[measure_keys.index('segment_beta')]
node_betweenness = measures_data[measure_keys.index('node_betweenness')]
node_betweenness_beta = measures_data[measure_keys.index('node_betweenness_beta')]
segment_betweenness = measures_data[measure_keys.index('segment_betweenness')]
# post compute improved
improved_closness = node_density / node_farness / node_density
# angular keys
measure_keys_angular = [
'node_harmonic_angular',
'segment_harmonic_hybrid',
'node_betweenness_angular',
'segment_betweeness_hybrid'
]
np.random.shuffle(measure_keys_angular) # in place
measure_keys_angular = tuple(measure_keys_angular)
# generate the angular measures
measures_data_angular = centrality.local_centrality(node_data,
edge_data,
node_edge_map,
distances,
betas,
measure_keys_angular,
angular=True)
node_harmonic_angular = measures_data_angular[measure_keys_angular.index('node_harmonic_angular')]
segment_harmonic_hybrid = measures_data_angular[measure_keys_angular.index('segment_harmonic_hybrid')]
node_betweenness_angular = measures_data_angular[measure_keys_angular.index('node_betweenness_angular')]
segment_betweeness_hybrid = measures_data_angular[measure_keys_angular.index('segment_betweeness_hybrid')]
# test node density
# node density count doesn't include self-node
# connected component == 48 == len(G) - 4
# isolated looping component == 3
# isolated edge == 1
# isolated node == 0
for n in node_density[4]: # infinite distance - exceeds cutoff clashes
assert n in [48, 3, 1, 0]
# test harmonic closeness vs NetworkX
nx_harm_cl = nx.harmonic_centrality(G_round_trip, distance='length')
nx_harm_cl = np.array([v for v in nx_harm_cl.values()])
assert np.allclose(nx_harm_cl, node_harmonic[4], atol=0.001, rtol=0)
# test betweenness vs NetworkX
# set endpoint counting to false and do not normalise
nx_betw = nx.betweenness_centrality(G_round_trip, weight='length', endpoints=False, normalized=False)
nx_betw = np.array([v for v in nx_betw.values()])
# for some reason nx betweenness gives 0.5 instead of 1 for disconnected looping component (should be 1)
# maybe two equidistant routes being divided through 2
# nx betweenness gives 0.5 instead of 1 for all disconnected looping component nodes
# nx presumably takes equidistant routes into account, in which case only the fraction is aggregated
assert np.allclose(nx_betw[:52], node_betweenness[4][:52], atol=0.001, rtol=0)
# test against various distances
for d_idx in range(len(distances)):
dist_cutoff = distances[d_idx]
beta = betas[d_idx]
# do the comparisons array-wise so that betweenness can be aggregated
betw = np.full(G.number_of_nodes(), 0.0)
betw_wt = np.full(G.number_of_nodes(), 0.0)
dens = np.full(G.number_of_nodes(), 0.0)
far_imp = np.full(G.number_of_nodes(), 0.0)
far_dist = np.full(G.number_of_nodes(), 0.0)
harmonic_cl = np.full(G.number_of_nodes(), 0.0)
grav = np.full(G.number_of_nodes(), 0.0)
cyc = np.full(G.number_of_nodes(), 0.0)
for src_idx in range(len(G)):
# get shortest path maps
tree_map, tree_edges = centrality.shortest_path_tree(edge_data,
node_edge_map,
src_idx,
dist_cutoff,
angular=False)
tree_preds = tree_map[:, 1]
tree_dists = tree_map[:, 2]
tree_imps = tree_map[:, 3]
tree_cycles = tree_map[:, 4]
for n_idx in G.nodes():
# skip self nodes
if n_idx == src_idx:
continue
# get distance and impedance
dist = tree_dists[n_idx]
imp = tree_imps[n_idx]
# continue if exceeds max
if np.isinf(dist) or dist > dist_cutoff:
continue
# aggregate values
dens[src_idx] += 1
far_imp[src_idx] += imp
far_dist[src_idx] += dist
harmonic_cl[src_idx] += 1 / imp
grav[src_idx] += np.exp(beta * dist)
# cycles
if tree_cycles[n_idx]:
cyc[src_idx] += 1
# BETWEENNESS
# only process betweenness in one direction
if n_idx < src_idx:
continue
# betweenness - only counting truly between vertices, not starting and ending verts
inter_idx = tree_preds[n_idx]
# isolated nodes will have no predecessors
if np.isnan(inter_idx):
continue
inter_idx = np.int(inter_idx)
while True:
# break out of while loop if the intermediary has reached the source node
if inter_idx == src_idx:
break
betw[inter_idx] += 1
betw_wt[inter_idx] += np.exp(beta * dist)
# follow
inter_idx = np.int(tree_preds[inter_idx])
improved_cl = dens / far_dist / dens
assert np.allclose(node_density[d_idx], dens, atol=0.001, rtol=0)
assert np.allclose(node_farness[d_idx], far_dist, atol=0.01, rtol=0) # relax precision
assert np.allclose(node_cycles[d_idx], cyc, atol=0.001, rtol=0)
assert np.allclose(node_harmonic[d_idx], harmonic_cl, atol=0.001, rtol=0)
assert np.allclose(node_beta[d_idx], grav, atol=0.001, rtol=0)
assert np.allclose(improved_closness[d_idx], improved_cl, equal_nan=True, atol=0.001, rtol=0)
assert np.allclose(node_betweenness[d_idx], betw, atol=0.001, rtol=0)
assert np.allclose(node_betweenness_beta[d_idx], betw_wt, atol=0.001, rtol=0)
# TODO: are there possibly ways to test segment_density, harmonic_segment, segment_beta, segment_betweenness
# for infinite distance, the segment density should match the sum of reachable segments
length_sum = 0
for s, e, d in G_round_trip.edges(data=True):
length_sum += d['length']
reachable_length_sum = length_sum - \
(G_round_trip[50][51]['length'] +
G_round_trip[52][53]['length'] +
G_round_trip[53][54]['length'] +
G_round_trip[54][55]['length'] +
G_round_trip[52][55]['length'])
assert np.allclose(segment_density[-1][:49], reachable_length_sum, atol=0.01, rtol=0) # relax precision
# check that problematic keys are caught
for angular, k in zip([False, True], ['node_harmonic', 'node_harmonic_angular']):
# catch typos
with pytest.raises(ValueError):
centrality.local_centrality(node_data,
edge_data,
node_edge_map,
distances,
betas,
('typo_key',),
angular=False)
# catch duplicates
with pytest.raises(ValueError):
centrality.local_centrality(node_data,
edge_data,
node_edge_map,
distances,
betas,
(k, k),
angular=False)
# catch mixed angular and non-angular keys
with pytest.raises(ValueError):
centrality.local_centrality(node_data,
edge_data,
node_edge_map,
distances,
betas,
('node_density', 'node_harmonic_angular'),
angular=False)