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scripts_i210_attack.py
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scripts_i210_attack.py
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from process_data import extract_features, process_links, geojson_link, \
process_net
from frank_wolfe import solver_3
import numpy as np
from metrics import cost_ratio, cost, save_metrics
# from heterogeneous_solver import gauss_seidel, jacobi
from multi_types_solver import parametric_study
from utils import multiply_cognitive_cost
def load_I210():
net = np.loadtxt('data/I210_attack_net.csv', delimiter=',', skiprows=1)
demand = np.loadtxt('data/I210_od.csv', delimiter=',', skiprows=1)
node = np.loadtxt('data/I210_node.csv', delimiter=',', skiprows=1)
geometry = extract_features('data/I210_attack_Sketch_net.csv')
demand = np.reshape(demand, (1, 3))
return net, demand, node, geometry
def process_I210_net():
process_net('data/I210_attack_Sketch_net.csv', 'data/I210_attack_net.csv')
def frank_wolfe_on_I210():
'''
Frank-Wolfe on I210
'''
graph = np.loadtxt('data/I210_attack_net.csv', delimiter=',', skiprows=1)
demand = np.loadtxt('data/I210_od.csv', delimiter=',', skiprows=1)
demand[:, 2] = 1. * demand[:, 2] / 4000
# run solver
f = solver_3(graph, demand, max_iter=1000, q=50, display=1, stop=1e-2)
# display cost
for a, b in zip(cost(f, graph), f * 4000):
print a, b
# visualization
node = np.loadtxt('data/I210_node.csv', delimiter=',', skiprows=1)
# extract features: 'capacity', 'length', 'fftt'
feat = extract_features('data/I210_attack_Sketch_net.csv')
ratio = cost_ratio(f, graph)
# merge features with the cost ratios
features = np.zeros((feat.shape[0], 4))
features[:, :3] = feat
features[:, 3] = ratio
# join features with (lat1,lon1,lat2,lon2)
links = process_links(graph, node, features)
color = features[:, 3] # we choose the costs
names = ['capacity', 'length', 'fftt', 'tt_over_fftt']
geojson_link(links, names, color)
def I210_ratio_r_total():
'''
study the test_*.csv files generated by I210_parametric_study()
in particular, visualize the ratio each type of users on each link
'''
fs = np.loadtxt('data/test_50.csv', delimiter=',', skiprows=0)
ratio = np.divide(fs[:, 1], np.maximum(np.sum(fs, axis=1), 1e-8))
net = np.loadtxt('data/I210_net.csv', delimiter=',', skiprows=1)
node = np.loadtxt('data/I210_node.csv', delimiter=',', skiprows=1)
geometry = extract_features('data/I210Sketch_net.csv')
features = np.zeros((fs.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = ratio
links = process_links(net, node, features)
color = 2 * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_routed'], color)
# print(fs)
# for a,b in zip(cost(f, graph), f*4000): print a,b
def I210_parametric_study(alphas):
g, d, node, geom = load_I210()
d[:, 2] = d[:, 2] / 4000.
parametric_study(alphas, g, d, node, geom, 3000., 100.,
'data/I210_attack/test_{}.csv',
stop=1e-3, stop_cycle=1e-3)
def I210_metrics(alphas):
net, d, node, features = load_I210()
d[:, 2] = d[:, 2] / 4000.
net2, small_capacity = multiply_cognitive_cost(net, features, 3000., 100.)
save_metrics(alphas, net, net2, d, features, small_capacity,
'data/I210_attack/test_{}.csv', 'data/I210_attack/out.csv',
skiprows=1)
def I210_path_choices(alphas):
out = np.zeros((len(alphas), 4))
for i, alpha in enumerate(alphas):
f_r = np.loadtxt(
'data/I210_attack/test_{}.csv'.format(int(alpha * 100)),
delimiter=',', skiprows=1)[:, 1]
out[i, 0] = alpha
if f_r[12] > 0.:
out[i, 3] = f_r[12] * 4000. # flow middle path
if f_r[0] > 0.:
out[i, 2] = f_r[0] * 4000. # flow up path
if f_r[13] > 0.:
out[i, 1] = f_r[13] * 4000. # flow low path
np.savetxt('data/I210_attack/path_flows_routed.csv', out, delimiter=',',
header='ratio_routed,low_path,hi_path,mid_path', comments='')
def check_results():
# check highest ration of tt / fftt (should be less than 5)
net, d, node, features = load_I210()
f = np.loadtxt('data/I210/test_0.csv', delimiter=',')
print np.max(cost_ratio(f, net))
def I210_ratio_r_nr(alpha):
'''
study the test_*.csv files generated by chicago_parametric_study()
in particular, visualize the ratio each type of users on each link
ratio of non-routed over total for each link
'''
fs = np.loadtxt(
'data/I210/test_{}.csv'.format(int(alpha * 100)),
delimiter=',', skiprows=0)
ratio = np.divide(fs[:, 1], np.maximum(np.sum(fs, axis=1), 1e-8))
net, demand, node, geometry = load_I210()
features = np.zeros((fs.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = ratio
links = process_links(net, node, features, in_order=True)
color = 5. * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_non_routed'], color)
def visual(alpha):
'''
study the test_*.csv files generated by chicago_parametric_study()
in particular, visualize the ratio each type of users on each link
ratio of non-routed over total for each link
'''
fs = np.loadtxt(
'data/I210/test_{}.csv'.format(int(alpha * 100)),
delimiter=',', skiprows=0)
f_total = 4000 * np.sum(fs, axis=1)
color = np.zeros(len(f_total))
v_thres = 6000
for i in range(len(f_total)):
if f_total[i] <= v_thres:
color[i] = 1
# elif f_total[i]<=2*v_thres:
# color[i]=2
# elif f_total[i]<=3*v_thres:
# color[i]=3
# elif f_total[i]<=4*v_thres:
# color[i]=4
else:
color[i] = 5
print(np.mean(color))
net, demand, node, geometry = load_I210()
features = np.zeros((fs.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = f_total
links = process_links(net, node, features, in_order=True)
# color = 5. * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_non_routed'], color)
def I210_capacities():
net, demand, node, features = load_I210()
links = process_links(net, node, features)
color = 2. * (features[:, 0] <= 3000.) + 5. * (features[:, 0] > 3000.)
weight = (features[:, 0] <= 3000.) + 2. * (features[:, 0] > 3000.)
geojson_link(links, ['capacity', 'length', 'fftt'], color, 2. * weight)
def main():
# process_I210_net()
# frank_wolfe_on_I210()
# I210_parametric_study()
# I210_ratio_r_total()
# I210_parametric_study_2()
# I210_parametric_study(np.linspace(0,.25,26))
# I210_metrics(np.linspace(0,.25,26))
# 210_ratio_r_nr(0.9)
# visual(0.5)
# check_results()
I210_path_choices(np.linspace(0, .25, 26))
# I210_capacities()
if __name__ == '__main__':
main()