def test_invalid_beta_throws(self):
distances = dok_matrix(hand_net_dists)
if issparse(distances):
hand_net_incidence_local = dok_matrix(hand_net_incidence)
else:
hand_net_incidence_local = hand_net_incidence
data_list = [distances]
network_struct = ModelDataStruct(data_list,
hand_net_incidence_local,
data_array_names_debug=("distances",
"u_turn"))
beta_vec = np.array([-5])
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_vec,
mu=1)
try:
model.generate_observations(origin_indices=[0, 1, 2, 7],
dest_indices=[1, 6, 3],
num_obs_per_pair=4,
iter_cap=15,
rng_seed=1)
except ValueError as e:
print(str(e))
def test_bad_beta_fails(self, struct_bigger):
model = RecursiveLogitModelPrediction(struct_bigger, initial_beta=-0.1)
with pytest.raises(ValueError) as e:
model.generate_observations(origin_indices=[0],
dest_indices=[9],
num_obs_per_pair=10)
assert "exp(V(s)) contains negative values" in str(e.value)
def test_bad_indexfails(self, struct_bigger):
model = RecursiveLogitModelPrediction(struct_bigger, initial_beta=-0.2)
with pytest.raises(IndexError) as e:
model.generate_observations(origin_indices=[0],
dest_indices=[100],
num_obs_per_pair=10)
assert "Can only simulate observations from indexes which are in the model" in str(
e.value)
with pytest.raises(IndexError) as e:
model.generate_observations(origin_indices=[0],
dest_indices=[10],
num_obs_per_pair=10)
assert "but the final index is reserved for internal dummy sink state" in str(
e.value)
def get_data(beta, seed=None):
beta_vec_generate = np.array([beta])
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_vec_generate, mu=1)
obs_indices = [0]
obs = model.generate_observations(origin_indices=obs_indices,
dest_indices=[1],
num_obs_per_pair=60, iter_cap=2000, rng_seed=seed,
)
return obs
def get_data(beta_vec, seed=None):
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_vec,
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
def _basic_consistencey_checks(distances):
data_list = [distances]
if issparse(distances):
hand_net_incidence_local = dok_matrix(hand_net_incidence)
else:
hand_net_incidence_local = hand_net_incidence
network_struct = ModelDataStruct(data_list,
hand_net_incidence_local,
data_array_names_debug=("distances",
"u_turn"))
beta_vec = np.array([-1])
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_vec,
mu=1)
obs = model.generate_observations(origin_indices=[0, 1, 2, 7],
dest_indices=[1, 6, 3],
num_obs_per_pair=4,
iter_cap=15,
rng_seed=1)
expected = TestSimulation._get_basic_consistency_expected(
ALLOW_POSITIVE_VALUE_FUNCTIONS)
assert obs == expected
data_list = [distances]
network_struct = ModelDataStruct(data_list, incidence_mat,
data_array_names_debug=("distances"))
beta_vec = np.array([-16])
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_vec, mu=1)
# obs_indices = [i for i in range(8)]
# obs = model.generate_observations(origin_indices=obs_indices,
# dest_indices=obs_indices,
# num_obs_per_pair=1, iter_cap=2000, rng_seed=1,
# )
obs_indices = [1, 2, 3, 4, 5, 6, 7, 8]
obs = model.generate_observations(origin_indices=obs_indices,
dest_indices=[7, 3],
num_obs_per_pair=20, iter_cap=2000, rng_seed=1,
)
print(obs)
print("\nPath in terms of arcs:")
for path in obs:
string = "Orig: "
f = "Empty Path, should not happen"
for arc_index in path:
string += f"-{arc_index + 1}- => "
string += ": Dest"
print(string)
obs_fname = "my_networks_obs2.json"
beta_sim = np.array([-0.8, -0.00015])
model = RecursiveLogitModelPrediction(network_struct,
initial_beta=beta_sim, mu=1)
print("Linear system size", model.get_exponential_utility_matrix().shape)
# sparse sample for quick running example
orig_indices = np.arange(0, node_max, 2)
dest_indices = (orig_indices + 5) % node_max
# sample every OD pair once
# orig_indices = np.arange(0, node_max, 1)
# dest_indices = np.arange(0, node_max, 1)
obs_per_pair = 1
print(f"Generating {obs_per_pair * len(orig_indices) * len(dest_indices)} obs total per "
f"configuration")
seed = 42
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)
optimiser = optimisers.ScipyOptimiser(method='l-bfgs-b')
beta_est_init = [-5, -0.00001]
model_est = RecursiveLogitModelEstimation(network_struct, observations_record=obs,
initial_beta=beta_est_init, mu=1,
optimiser=optimiser)
beta_est = model_est.solve_for_optimal_beta(verbose=False)
print(f"beta expected: [{beta_sim[0]:6.4f}, {beta_sim[1]:6.4f}],"
f" beta_actual: [{beta_est[0]:6.4f}, {beta_est[1]:6.4f}]")
def test_est():
assert np.allclose([-1.02129578, -0.000175462809], beta_est)