def consistency_test(network_file,
orig_indices,
dest_indices,
obs_per_pair,
beta0,
test_range=None):
if test_range is None:
test_range = np.arange(-0.1, -2.1, -0.1)
# network_file = "EMA_net.tntp"
data_list, data_list_names = load_tntp_node_formulation(
network_file,
columns_to_extract=[
"length",
],
)
distances = data_list[0]
incidence_mat = (distances > 0).astype(int)
network_struct = ModelDataStruct(data_list,
incidence_mat,
data_array_names_debug=("distances",
"u_turn"))
beta_vec = np.array([-0.1])
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_vec,
mu=1)
print("Linear system size", model.get_exponential_utility_matrix().shape)
print(
f"Generating {obs_per_pair * len(orig_indices) * len(dest_indices)} obs total per "
f"beta sim val")
def get_data(beta_vec, seed=None):
beta_vec_generate = np.array([beta_vec])
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_vec_generate,
mu=1)
obs = model.generate_observations(
origin_indices=orig_indices,
dest_indices=dest_indices,
num_obs_per_pair=obs_per_pair,
iter_cap=2000,
rng_seed=seed,
)
return obs
optimiser = optimisers.ScipyOptimiser(method='l-bfgs-b') # bfgs, l-bfgs-b
import time
a = time.time()
expected = []
actual = []
for n, beta_gen in enumerate(test_range, start=1):
expected.append(beta_gen)
try:
obs = get_data(beta_gen, seed=None)
except ValueError as e:
print(f"beta = {beta_gen} failed, {e}")
actual.append(0.0)
continue
# print(obs)
beta0 = -5
model = RecursiveLogitModelEstimation(network_struct,
observations_record=obs,
initial_beta=beta0,
mu=1,
optimiser=optimiser)
beta = model.solve_for_optimal_beta(verbose=False)
actual.append(float(beta))
print("beta_expected", beta_gen, "beta actual", beta, "\nOBS:")
# text_list = wrapper.wrap(str(obs))
# print("\n".join(text_list))
b = time.time()
print("elapsed =", b - a, "s")
return np.array(expected), np.array(actual)
# distances +=1
data_list = [distances]
nz_dist = distances.reshape(distances.shape[0] * distances.shape[1], 1)
nz_dist = nz_dist[nz_dist > 0]
print("(max dist, min dist, mean dist) = ",
(np.max(nz_dist), np.min(nz_dist), np.mean(nz_dist), np.std(nz_dist)))
network_struct = ModelDataStruct(data_list,
incidence_mat,
data_array_names_debug=("distances",
"u_turn"))
beta_vec = np.array([-1])
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_vec,
mu=1)
print("Linear system size", model.get_exponential_utility_matrix().shape)
orig_indices = np.arange(0, arcmaxp1, 30)
dest_indices = (orig_indices + 5) % arcmaxp1
# orig_indices = np.arange(0, 7, 1)
# dest_indices = np.arange(0, 7, 1)
obs_per_pair = 1
print(
f"Generating {obs_per_pair * len(orig_indices) * len(dest_indices)} obs total per "
f"configuration")
def get_data(beta, seed=None):
beta_vec_generate = np.array([beta])
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_vec_generate,