def test_all_or_nothing_assignment(self):
net = np.loadtxt('data/braess_net.csv', delimiter=',', skiprows=1)
cost = np.array([1.,5.,1.,5.,1.])
demand = np.loadtxt('data/braess_od.csv', delimiter=',', skiprows=1)
demand=np.reshape(demand, (1,3))
L = all_or_nothing_assignment(cost, net, demand)
self.assertTrue(np.linalg.norm(L - np.array([2.,0.,2.,0.,2.])) < 1e-8)
def test_all_or_nothing_assignment(self):
net = np.loadtxt('data/braess_net.csv', delimiter=',', skiprows=1)
cost = np.array([1., 5., 1., 5., 1.])
demand = np.loadtxt('data/braess_od.csv', delimiter=',', skiprows=1)
demand = np.reshape(demand, (1, 3))
L = all_or_nothing_assignment(cost, net, demand)
self.assertTrue(
np.linalg.norm(L - np.array([2., 0., 2., 0., 2.])) < 1e-8)
def check_LA_result():
net, demand, node, features = load_LA_2()
demand[:, 2] = demand[:, 2] / 4000.
f = np.loadtxt('data/LA/LA_output_4.csv', delimiter=',', skiprows=0)
costs = cost(f, net)
cr = cost_ratio(f, net)
print np.sort(cr)[-20:]
for row in range(net.shape[0]):
if cr[row] >= 10.:
print cr[row]
print net[row, :3], features[row, :]
L = all_or_nothing_assignment(costs, net, demand)
print costs.dot(L) / np.sum(demand[:, 2])
def check_LA_result():
net, demand, node, features = load_LA_2()
demand[:,2] = demand[:,2] / 4000.
f = np.loadtxt('data/LA/LA_output_4.csv', delimiter=',', skiprows=0)
costs = cost(f, net)
cr = cost_ratio(f, net)
print np.sort(cr)[-20:]
for row in range(net.shape[0]):
if cr[row] >= 10.:
print cr[row]
print net[row,:3], features[row,:]
L = all_or_nothing_assignment(costs, net, demand)
print costs.dot(L) / np.sum(demand[:,2])
def compute_metrics_beta(alpha, beta, f, net, d, feat, subset, out, row, fs=None, net2=None, \
length_unit='Mile', time_unit='Minute'):
'''
Save in the numpy array 'out' at the specific 'row' the following metrics
- average cost for non-routed
- average cost for routed
- average cost
- average cost on a subset (e.g. local routes)
- average cost outside of a subset (e.g. non-local routes)
- total gas emissions
- total gas emissions on a subset (e.g. local routes)
- total gas emissions outside of a subset (e.g. non-local routes)
- total flow in the network
- total flow in the network on a subset (e.g. local routes)
- total flow in the network outside of a subset (e.g. non-local routes)
'''
if length_unit == 'Meter':
lengths = feat[:, 1] / 1609.34 # convert into miles
elif length_unit == 'Mile':
lengths = feat[:, 1]
if time_unit == 'Minute':
a = 60.0
elif time_unit == 'Second':
a = 3600.
b = 60. / a
speed = a * np.divide(lengths, np.maximum(cost(f, net), 10e-8))
co2 = np.multiply(gas_emission(speed), lengths)
out[row, 0] = alpha
out[row, 1] = beta
out[row, 4] = b * average_cost(f, net, d)
out[row, 5] = b * average_cost_subset(f, net, d, subset)
out[row, 6] = out[row, 3] - out[row, 4]
out[row, 7] = co2.dot(f) / f.dot(lengths)
out[row, 8] = np.multiply(co2, subset).dot(f) / f.dot(lengths)
out[row, 9] = out[row, 6] - out[row, 7]
out[row, 10] = np.sum(np.multiply(f, lengths)) * 4000.
out[row, 11] = np.sum(np.multiply(np.multiply(f, lengths), subset)) * 4000.
out[row, 12] = out[row, 9] - out[row, 10]
if alpha == 0.0:
out[row, 2] = b * average_cost(f, net, d)
out[row, 3] = b * average_cost_all_or_nothing(f, net, d)
return
if alpha == 1.0:
L = all_or_nothing_assignment(cost(f, net2), net, d)
out[row, 2] = b * cost(f, net).dot(L) / np.sum(d[:, 2])
out[row, 3] = b * average_cost(f, net, d)
return
out[row, 2] = b * cost(f, net).dot(fs[:, 0]) / np.sum(
(1 - alpha) * d[:, 2])
out[row, 3] = b * cost(f, net).dot(fs[:, 1]) / np.sum(alpha * d[:, 2])
def compute_metrics_beta(alpha, beta, f, net, d, feat, subset, out, row, fs=None, net2=None, \
length_unit='Mile', time_unit='Minute'):
'''
Save in the numpy array 'out' at the specific 'row' the following metrics
- average cost for non-routed
- average cost for routed
- average cost
- average cost on a subset (e.g. local routes)
- average cost outside of a subset (e.g. non-local routes)
- total gas emissions
- total gas emissions on a subset (e.g. local routes)
- total gas emissions outside of a subset (e.g. non-local routes)
- total flow in the network
- total flow in the network on a subset (e.g. local routes)
- total flow in the network outside of a subset (e.g. non-local routes)
'''
if length_unit == 'Meter':
lengths = feat[:,1] / 1609.34 # convert into miles
elif length_unit == 'Mile':
lengths = feat[:,1]
if time_unit == 'Minute':
a = 60.0
elif time_unit == 'Second':
a = 3600.
b = 60./a
speed = a * np.divide(lengths, np.maximum(cost(f, net), 10e-8))
co2 = np.multiply(gas_emission(speed), lengths)
out[row,0] = alpha
out[row,1] = beta
out[row,4] = b * average_cost(f, net, d)
out[row,5] = b * average_cost_subset(f, net, d, subset)
out[row,6] = out[row,3] - out[row,4]
out[row,7] = co2.dot(f) / f.dot(lengths)
out[row,8] = np.multiply(co2, subset).dot(f) / f.dot(lengths)
out[row,9] = out[row,6] - out[row,7]
out[row,10] = np.sum(np.multiply(f, lengths)) * 4000.
out[row,11] = np.sum(np.multiply(np.multiply(f, lengths), subset)) * 4000.
out[row,12] = out[row,9] - out[row,10]
if alpha == 0.0:
out[row,2] = b * average_cost(f, net, d)
out[row,3] = b * average_cost_all_or_nothing(f, net, d)
return
if alpha == 1.0:
L = all_or_nothing_assignment(cost(f, net2), net, d)
out[row,2] = b * cost(f, net).dot(L) / np.sum(d[:,2])
out[row,3] = b * average_cost(f, net, d)
return
out[row,2] = b * cost(f, net).dot(fs[:,0]) / np.sum((1-alpha)*d[:,2])
out[row,3] = b * cost(f, net).dot(fs[:,1]) / np.sum(alpha*d[:,2])
