def test_example_tiny_smart_loading(self):
subfolder = "ExampleTiny" # big data from classical v2
folder = join("Datasets", subfolder)
obs_mat, attrs = load_standard_path_format_csv(folder,
delim=" ",
angles_included=False)
incidence_mat, travel_times_mat = attrs
# left, right, _, u_turn = AngleProcessor.get_turn_categorical_matrices()
self._first_example_common_data_checks(travel_times_mat, incidence_mat,
obs_mat)
def test_compare_optim_methods(self):
subfolder = "ExampleTinyModifiedObs" # big data from classical v2
folder = join("Datasets", subfolder)
obs_mat, attrs = load_standard_path_format_csv(folder,
delim=" ",
angles_included=True)
import awkward1 as ak
obs_mat = obs_mat.toarray()
obs_record = ak.from_numpy(obs_mat)
incidence_mat, travel_times_mat, angle_cts_mat = attrs
left, _, _, u_turn = AngleProcessor.get_turn_categorical_matrices(
angle_cts_mat, incidence_mat)
data_list = [travel_times_mat, left, u_turn]
network_data_struct = ModelDataStruct(data_list, incidence_mat)
# network_data_struct.add_second_travel_time_for_testing()
optimiser = optimisers.LineSearchOptimiser(
optimisers.OptimHessianType.BFGS, max_iter=4)
RecursiveLogitModelEstimation.zeros_error_override = False
model = RecursiveLogitModelEstimation(network_data_struct,
optimiser,
observations_record=obs_record,
initial_beta=-15)
m1_ll_out, m1_grad_out = model.get_log_likelihood()
optimiser2 = optimisers.ScipyOptimiser(method='newton-cg')
model2 = RecursiveLogitModelEstimation(network_data_struct,
optimiser2,
observations_record=obs_record,
initial_beta=-15)
m2_ll_out, m2_grad_out = model2.get_log_likelihood()
assert np.allclose(m2_ll_out, m1_ll_out)
assert np.allclose(m2_grad_out, m1_grad_out)
beta1 = model.solve_for_optimal_beta()
beta2 = model2.solve_for_optimal_beta(verbose=True)
m1_ll_out, m1_grad_out = model.get_log_likelihood()
m2_ll_out, m2_grad_out = model2.get_log_likelihood()
print(m1_ll_out, m2_ll_out)
print(m1_grad_out, m2_grad_out)
assert np.allclose(beta1, beta2, 0.34657)
RecursiveLogitModelEstimation.zeros_error_override = None
def test_example_tiny_modified_awkward_array(self):
subfolder = "ExampleTinyModifiedObs" # big data from classical v2
folder = join("Datasets", subfolder)
obs_mat, attrs = load_standard_path_format_csv(folder,
delim=" ",
angles_included=True)
import awkward1 as ak
obs_mat = obs_mat.toarray()
obs_record = ak.from_numpy(obs_mat)
incidence_mat, travel_times_mat, angle_cts_mat = attrs
left, _, _, u_turn = AngleProcessor.get_turn_categorical_matrices(
angle_cts_mat, incidence_mat)
# incidence matrix which only has nonzero travel times
# - rather than what is specified in file
t_time_incidence = (travel_times_mat > 0).astype('int').todok()
self._tiny_modified_common_data_checks(travel_times_mat, left, u_turn,
t_time_incidence, incidence_mat,
obs_record)
def test_example_tiny_modified_sparse(self):
# TODO shouldn't be using this data - data is just confusing/ unclear, nothing inherently
# wrong
# Now is a bad example as @42f564e9 results in this test case being illegal valued
subfolder = "ExampleTinyModifiedObs" # big data from classical v2
folder = join("Datasets", subfolder)
obs_mat, attrs = load_standard_path_format_csv(folder,
delim=" ",
angles_included=True)
incidence_mat, travel_times_mat, angle_cts_mat = attrs
left, _, _, u_turn = AngleProcessor.get_turn_categorical_matrices(
angle_cts_mat, incidence_mat)
# incidence matrix which only has nonzero travel times
# - rather than what is specified in file
t_time_incidence = (travel_times_mat > 0).astype('int').todok()
self._tiny_modified_common_data_checks(travel_times_mat, left, u_turn,
t_time_incidence, incidence_mat,
obs_mat)
def test_example_tiny_modified_awkward_array_in_expected_format(self):
"""Test's awkward array input obs format when it is actually zero indexed and ragged
data, not square. See that output is consistent in this case"""
subfolder = "ExampleTinyModifiedObs" # big data from classical v2
folder = join("Datasets", subfolder)
obs_mat, attrs = load_standard_path_format_csv(folder,
delim=" ",
angles_included=True)
import awkward1 as ak
obs_mat = obs_mat.toarray()
obs_list_raw = obs_mat.tolist()
# we know that obs mat is square and 1 indexed with zero for padding (sparse originally)
obs_conv = [[(i - 1) for i in row if i != 0] for row in obs_list_raw]
obs_record = ak.from_iter(obs_conv)
incidence_mat, travel_times_mat, angle_cts_mat = attrs
left, _, _, u_turn = AngleProcessor.get_turn_categorical_matrices(
angle_cts_mat, incidence_mat)
# incidence matrix which only has nonzero travel times
# - rather than what is specified in file
t_time_incidence = (travel_times_mat > 0).astype('int').todok()
self._tiny_modified_common_data_checks(travel_times_mat, left, u_turn,
t_time_incidence, incidence_mat,
obs_record)
# np.seterr(all='raise') # all='print')
np.set_printoptions(precision=12, suppress=True)
np.set_printoptions(edgeitems=10, linewidth=180)
np.core.arrayprint._line_width = 200
# import warnings
# warnings.simplefilter("error")
time_io_start = time.time()
# subfolder ="ExampleTutorial"# "ExampleTutorial" from classical logicv2
# subfolder = "ExampleTiny"
subfolder = "ExampleTinyModifiedObs"
print(os.getcwd())
folder = os.path.join("Datasets", subfolder)
obs_mat, attrs = load_standard_path_format_csv(folder,
delim=" ",
angles_included=True)
incidence_mat, travel_times_mat, angle_cts_mat = attrs
left, _, _, u_turn = AngleProcessor.get_turn_categorical_matrices(
angle_cts_mat, incidence_mat)
# incidence matrix which only has nonzero travel times - rather than what is specified in file
t_time_incidence = (travel_times_mat > 0).astype('int').todok()
data_list = [travel_times_mat, left, u_turn, t_time_incidence]
network_data_struct = ModelDataStruct(data_list, incidence_mat)
#
# network_data_struct, obs_mat = ModelDataStruct.from_directory(
# folder, add_angles=True, angle_type='comparison', delim=" ")
#
# print(network_data_struct.data_array)
#
# print(network_data_struct.data_array)
