def test_make_cost_function_fourth(self):
"""Ensures correct output from make_cost_function.
In this case, using fourth set of inputs.
"""
this_cost_function = permutation.make_cost_function(
heating_rate_weight=FOURTH_HEATING_RATE_WEIGHT,
flux_weight=FOURTH_FLUX_WEIGHT,
include_net_flux=FOURTH_INCLUDE_NET_FLUX_FLAG
)
this_cost = this_cost_function(
target_matrices=TARGET_MATRICES,
prediction_matrices=PREDICTION_MATRICES
)
self.assertTrue(numpy.isclose(this_cost, FOURTH_COST, atol=TOLERANCE))
def _run(model_file_name, example_file_name, num_examples, example_dir_name,
example_id_file_name, heating_rate_weight, flux_weight,
include_net_flux, do_backwards_test, shuffle_profiles_together,
num_bootstrap_reps, output_file_name):
"""Runs permutation-based importance test.
This is effectively the main method.
:param model_file_name: See documentation at top of file.
:param example_file_name: Same.
:param num_examples: Same.
:param example_dir_name: Same.
:param example_id_file_name: Same.
:param heating_rate_weight: Same.
:param flux_weight: Same.
:param include_net_flux: Same.
:param do_backwards_test: Same.
:param shuffle_profiles_together: Same.
:param num_bootstrap_reps: Same.
:param output_file_name: Same.
"""
cost_function = permutation.make_cost_function(
heating_rate_weight=heating_rate_weight, flux_weight=flux_weight,
include_net_flux=include_net_flux
)
print('Reading model from: "{0:s}"...'.format(model_file_name))
model_object = neural_net.read_model(model_file_name)
metafile_name = neural_net.find_metafile(
model_dir_name=os.path.split(model_file_name)[0],
raise_error_if_missing=True
)
print('Reading metadata from: "{0:s}"...'.format(metafile_name))
metadata_dict = neural_net.read_metafile(metafile_name)
predictor_matrix, target_matrices = (
misc_utils.get_examples_for_inference(
model_metadata_dict=metadata_dict,
example_file_name=example_file_name,
num_examples=num_examples, example_dir_name=example_dir_name,
example_id_file_name=example_id_file_name
)[:2]
)
print(SEPARATOR_STRING)
if not isinstance(target_matrices, list):
target_matrices = [target_matrices]
if do_backwards_test:
result_dict = permutation.run_backwards_test(
predictor_matrix=predictor_matrix, target_matrices=target_matrices,
model_object=model_object, model_metadata_dict=metadata_dict,
cost_function=cost_function,
shuffle_profiles_together=shuffle_profiles_together,
num_bootstrap_reps=num_bootstrap_reps
)
else:
result_dict = permutation.run_forward_test(
predictor_matrix=predictor_matrix, target_matrices=target_matrices,
model_object=model_object, model_metadata_dict=metadata_dict,
cost_function=cost_function,
shuffle_profiles_together=shuffle_profiles_together,
num_bootstrap_reps=num_bootstrap_reps
)
print(SEPARATOR_STRING)
print('Writing results of permutation test to: "{0:s}"...'.format(
output_file_name
))
permutation.write_file(
result_dict=result_dict, netcdf_file_name=output_file_name
)