def test_subset_by_height(self):
"""Ensures correct output from subset_by_height."""
this_example_dict = example_utils.subset_by_height(
example_dict=copy.deepcopy(FIRST_EXAMPLE_DICT),
heights_m_agl=HEIGHTS_TO_KEEP_M_AGL)
self.assertTrue(
_compare_example_dicts(this_example_dict,
FIRST_EXAMPLE_DICT_SELECT_HEIGHTS))
def test_subset_by_height_with_padding(self):
"""Ensures correct output from subset_by_height.
In this case, some of the desired heights are not there yet (and should
be added at the top of the profile).
"""
this_example_dict = example_utils.subset_by_height(
example_dict=copy.deepcopy(EXAMPLE_DICT_SANS_PADDING),
heights_m_agl=PADDED_HEIGHTS_TO_KEEP_M_AGL)
self.assertTrue(
_compare_example_dicts(this_example_dict,
EXAMPLE_DICT_PADDED_SELECT_HEIGHTS))
def _run(model_file_name, example_dir_name, first_time_string,
last_time_string, exclude_summit_greenland, output_file_name):
"""Applies trained neural net in inference mode.
This is effectively the main method.
:param model_file_name: See documentation at top of file.
:param example_dir_name: Same.
:param first_time_string: Same.
:param last_time_string: Same.
:param exclude_summit_greenland: Same.
:param output_file_name: Same.
"""
first_time_unix_sec = time_conversion.string_to_unix_sec(
first_time_string, TIME_FORMAT)
last_time_unix_sec = time_conversion.string_to_unix_sec(
last_time_string, TIME_FORMAT)
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)
generator_option_dict = copy.deepcopy(
metadata_dict[neural_net.TRAINING_OPTIONS_KEY])
generator_option_dict[neural_net.EXAMPLE_DIRECTORY_KEY] = example_dir_name
generator_option_dict[neural_net.FIRST_TIME_KEY] = first_time_unix_sec
generator_option_dict[neural_net.LAST_TIME_KEY] = last_time_unix_sec
vector_target_norm_type_string = copy.deepcopy(
generator_option_dict[neural_net.VECTOR_TARGET_NORM_TYPE_KEY])
scalar_target_norm_type_string = copy.deepcopy(
generator_option_dict[neural_net.SCALAR_TARGET_NORM_TYPE_KEY])
generator_option_dict[neural_net.VECTOR_TARGET_NORM_TYPE_KEY] = None
generator_option_dict[neural_net.SCALAR_TARGET_NORM_TYPE_KEY] = None
net_type_string = metadata_dict[neural_net.NET_TYPE_KEY]
predictor_matrix, target_array, example_id_strings = neural_net.create_data(
option_dict=generator_option_dict,
for_inference=True,
net_type_string=net_type_string,
exclude_summit_greenland=exclude_summit_greenland)
print(SEPARATOR_STRING)
exec_start_time_unix_sec = time.time()
prediction_array = neural_net.apply_model(
model_object=model_object,
predictor_matrix=predictor_matrix,
num_examples_per_batch=NUM_EXAMPLES_PER_BATCH,
net_type_string=net_type_string,
verbose=True)
print(SEPARATOR_STRING)
print('Time to apply neural net = {0:.4f} seconds'.format(
time.time() - exec_start_time_unix_sec))
vector_target_matrix = target_array[0]
vector_prediction_matrix = prediction_array[0]
if len(target_array) == 2:
scalar_target_matrix = target_array[1]
scalar_prediction_matrix = prediction_array[1]
else:
scalar_target_matrix = None
scalar_prediction_matrix = None
target_example_dict = _targets_numpy_to_dict(
scalar_target_matrix=scalar_target_matrix,
vector_target_matrix=vector_target_matrix,
model_metadata_dict=metadata_dict)
prediction_example_dict = _targets_numpy_to_dict(
scalar_target_matrix=scalar_prediction_matrix,
vector_target_matrix=vector_prediction_matrix,
model_metadata_dict=metadata_dict)
normalization_file_name = (
generator_option_dict[neural_net.NORMALIZATION_FILE_KEY])
print(('Reading training examples (for normalization) from: "{0:s}"...'
).format(normalization_file_name))
training_example_dict = example_io.read_file(normalization_file_name)
training_example_dict = example_utils.subset_by_height(
example_dict=training_example_dict,
heights_m_agl=generator_option_dict[neural_net.HEIGHTS_KEY])
num_examples = len(example_id_strings)
num_heights = len(prediction_example_dict[example_utils.HEIGHTS_KEY])
this_dict = {
example_utils.VECTOR_PREDICTOR_NAMES_KEY: [],
example_utils.VECTOR_PREDICTOR_VALS_KEY:
numpy.full((num_examples, num_heights, 0), 0.),
example_utils.SCALAR_PREDICTOR_NAMES_KEY: [],
example_utils.SCALAR_PREDICTOR_VALS_KEY:
numpy.full((num_examples, 0), 0.)
}
target_example_dict.update(this_dict)
prediction_example_dict.update(this_dict)
if vector_target_norm_type_string is not None:
print('Denormalizing predicted vectors...')
# down_flux_inc_matrix_w_m03 = example_utils.get_field_from_dict(
# example_dict=prediction_example_dict,
# field_name=example_utils.SHORTWAVE_DOWN_FLUX_INC_NAME
# )
# print(down_flux_inc_matrix_w_m03[0, ...])
# print('\n')
prediction_example_dict = normalization.denormalize_data(
new_example_dict=prediction_example_dict,
training_example_dict=training_example_dict,
normalization_type_string=vector_target_norm_type_string,
min_normalized_value=generator_option_dict[
neural_net.VECTOR_TARGET_MIN_VALUE_KEY],
max_normalized_value=generator_option_dict[
neural_net.VECTOR_TARGET_MAX_VALUE_KEY],
separate_heights=True,
apply_to_predictors=False,
apply_to_vector_targets=True,
apply_to_scalar_targets=False)
# down_flux_inc_matrix_w_m03 = example_utils.get_field_from_dict(
# example_dict=prediction_example_dict,
# field_name=example_utils.SHORTWAVE_DOWN_FLUX_INC_NAME
# )
# print(down_flux_inc_matrix_w_m03[0, ...])
# print('\n\n\n')
if scalar_target_norm_type_string is not None:
print('Denormalizing predicted scalars...')
prediction_example_dict = normalization.denormalize_data(
new_example_dict=prediction_example_dict,
training_example_dict=training_example_dict,
normalization_type_string=scalar_target_norm_type_string,
min_normalized_value=generator_option_dict[
neural_net.SCALAR_TARGET_MIN_VALUE_KEY],
max_normalized_value=generator_option_dict[
neural_net.SCALAR_TARGET_MAX_VALUE_KEY],
separate_heights=True,
apply_to_predictors=False,
apply_to_vector_targets=False,
apply_to_scalar_targets=True)
add_heating_rate = generator_option_dict[neural_net.OMIT_HEATING_RATE_KEY]
if add_heating_rate:
pressure_matrix_pascals = _get_unnormalized_pressure(
model_metadata_dict=metadata_dict,
example_id_strings=example_id_strings)
prediction_example_dict = _get_predicted_heating_rates(
prediction_example_dict=prediction_example_dict,
pressure_matrix_pascals=pressure_matrix_pascals,
model_metadata_dict=metadata_dict)
vector_target_names = (
generator_option_dict[neural_net.VECTOR_TARGET_NAMES_KEY])
if example_utils.SHORTWAVE_HEATING_RATE_NAME in vector_target_names:
heating_rate_index = vector_target_names.index(
example_utils.SHORTWAVE_HEATING_RATE_NAME)
heights_m_agl = generator_option_dict[neural_net.HEIGHTS_KEY]
height_indices = numpy.where(
heights_m_agl >= ZERO_HEATING_HEIGHT_M_AGL)[0]
vector_target_matrix = (
prediction_example_dict[example_utils.VECTOR_TARGET_VALS_KEY])
vector_target_matrix[..., heating_rate_index][..., height_indices] = 0.
prediction_example_dict[example_utils.VECTOR_TARGET_VALS_KEY] = (
vector_target_matrix)
all_heights_m_agl = generator_option_dict[neural_net.HEIGHTS_KEY]
desired_heights_m_agl = (
all_heights_m_agl[all_heights_m_agl < MAX_HEIGHT_M_AGL])
target_example_dict = example_utils.subset_by_height(
example_dict=target_example_dict, heights_m_agl=desired_heights_m_agl)
prediction_example_dict = example_utils.subset_by_height(
example_dict=prediction_example_dict,
heights_m_agl=desired_heights_m_agl)
print('Writing target (actual) and predicted values to: "{0:s}"...'.format(
output_file_name))
prediction_io.write_file(netcdf_file_name=output_file_name,
scalar_target_matrix=target_example_dict[
example_utils.SCALAR_TARGET_VALS_KEY],
vector_target_matrix=target_example_dict[
example_utils.VECTOR_TARGET_VALS_KEY],
scalar_prediction_matrix=prediction_example_dict[
example_utils.SCALAR_TARGET_VALS_KEY],
vector_prediction_matrix=prediction_example_dict[
example_utils.VECTOR_TARGET_VALS_KEY],
heights_m_agl=desired_heights_m_agl,
example_id_strings=example_id_strings,
model_file_name=model_file_name)
def _run(evaluation_file_names, line_styles, line_colour_strings,
set_descriptions_verbose, confidence_level, use_log_scale,
plot_by_height, output_dir_name):
"""Plots model evaluation.
This is effectively the main method.
:param evaluation_file_names: See documentation at top of file.
:param line_styles: Same.
:param line_colour_strings: Same.
:param set_descriptions_verbose: Same.
:param confidence_level: Same.
:param use_log_scale: Same.
:param plot_by_height: Same.
:param output_dir_name: Same.
"""
# Check input args.
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
if confidence_level < 0:
confidence_level = None
if confidence_level is not None:
error_checking.assert_is_geq(confidence_level, 0.9)
error_checking.assert_is_less_than(confidence_level, 1.)
num_evaluation_sets = len(evaluation_file_names)
expected_dim = numpy.array([num_evaluation_sets], dtype=int)
error_checking.assert_is_string_list(line_styles)
error_checking.assert_is_numpy_array(numpy.array(line_styles),
exact_dimensions=expected_dim)
error_checking.assert_is_string_list(set_descriptions_verbose)
error_checking.assert_is_numpy_array(numpy.array(set_descriptions_verbose),
exact_dimensions=expected_dim)
set_descriptions_verbose = [
s.replace('_', ' ') for s in set_descriptions_verbose
]
set_descriptions_abbrev = [
s.lower().replace(' ', '-') for s in set_descriptions_verbose
]
error_checking.assert_is_string_list(line_colour_strings)
error_checking.assert_is_numpy_array(numpy.array(line_colour_strings),
exact_dimensions=expected_dim)
line_colours = [
numpy.fromstring(s, dtype=float, sep='_') / 255
for s in line_colour_strings
]
for i in range(num_evaluation_sets):
error_checking.assert_is_numpy_array(line_colours[i],
exact_dimensions=numpy.array(
[3], dtype=int))
error_checking.assert_is_geq_numpy_array(line_colours[i], 0.)
error_checking.assert_is_leq_numpy_array(line_colours[i], 1.)
# Read files.
evaluation_tables_xarray = [xarray.Dataset()] * num_evaluation_sets
prediction_dicts = [dict()] * num_evaluation_sets
for i in range(num_evaluation_sets):
print('Reading data from: "{0:s}"...'.format(evaluation_file_names[i]))
evaluation_tables_xarray[i] = evaluation.read_file(
evaluation_file_names[i])
this_prediction_file_name = (
evaluation_tables_xarray[i].attrs[evaluation.PREDICTION_FILE_KEY])
print(
'Reading data from: "{0:s}"...'.format(this_prediction_file_name))
prediction_dicts[i] = prediction_io.read_file(
this_prediction_file_name)
model_file_name = (
evaluation_tables_xarray[0].attrs[evaluation.MODEL_FILE_KEY])
model_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(model_metafile_name))
model_metadata_dict = neural_net.read_metafile(model_metafile_name)
generator_option_dict = model_metadata_dict[
neural_net.TRAINING_OPTIONS_KEY]
scalar_target_names = (
generator_option_dict[neural_net.SCALAR_TARGET_NAMES_KEY])
vector_target_names = (
generator_option_dict[neural_net.VECTOR_TARGET_NAMES_KEY])
heights_m_agl = generator_option_dict[neural_net.HEIGHTS_KEY]
try:
t = evaluation_tables_xarray[0]
aux_target_names = t.coords[evaluation.AUX_TARGET_FIELD_DIM].values
except:
aux_target_names = []
num_scalar_targets = len(scalar_target_names)
num_vector_targets = len(vector_target_names)
num_heights = len(heights_m_agl)
num_aux_targets = len(aux_target_names)
example_dict = {
example_utils.SCALAR_TARGET_NAMES_KEY:
scalar_target_names,
example_utils.VECTOR_TARGET_NAMES_KEY:
vector_target_names,
example_utils.HEIGHTS_KEY:
heights_m_agl,
example_utils.SCALAR_PREDICTOR_NAMES_KEY:
generator_option_dict[neural_net.SCALAR_PREDICTOR_NAMES_KEY],
example_utils.VECTOR_PREDICTOR_NAMES_KEY:
generator_option_dict[neural_net.VECTOR_PREDICTOR_NAMES_KEY]
}
normalization_file_name = (
generator_option_dict[neural_net.NORMALIZATION_FILE_KEY])
print(('Reading training examples (for climatology) from: "{0:s}"...'
).format(normalization_file_name))
training_example_dict = example_io.read_file(normalization_file_name)
training_example_dict = example_utils.subset_by_height(
example_dict=training_example_dict, heights_m_agl=heights_m_agl)
mean_training_example_dict = normalization.create_mean_example(
new_example_dict=example_dict,
training_example_dict=training_example_dict)
print(SEPARATOR_STRING)
# Do actual stuff.
_plot_error_distributions(
prediction_dicts=prediction_dicts,
model_metadata_dict=model_metadata_dict,
aux_target_names=aux_target_names,
set_descriptions_abbrev=set_descriptions_abbrev,
set_descriptions_verbose=set_descriptions_verbose,
output_dir_name=output_dir_name)
print(SEPARATOR_STRING)
_plot_reliability_by_height(
evaluation_tables_xarray=evaluation_tables_xarray,
vector_target_names=vector_target_names,
heights_m_agl=heights_m_agl,
set_descriptions_abbrev=set_descriptions_abbrev,
set_descriptions_verbose=set_descriptions_verbose,
output_dir_name=output_dir_name)
print(SEPARATOR_STRING)
for k in range(num_vector_targets):
for this_score_name in list(SCORE_NAME_TO_PROFILE_KEY.keys()):
_plot_score_profile(
evaluation_tables_xarray=evaluation_tables_xarray,
line_styles=line_styles,
line_colours=line_colours,
set_descriptions_verbose=set_descriptions_verbose,
confidence_level=confidence_level,
target_name=vector_target_names[k],
score_name=this_score_name,
use_log_scale=use_log_scale,
output_dir_name=output_dir_name)
print(SEPARATOR_STRING)
for k in range(num_scalar_targets):
_plot_attributes_diagram(
evaluation_tables_xarray=evaluation_tables_xarray,
line_styles=line_styles,
line_colours=line_colours,
set_descriptions_abbrev=set_descriptions_abbrev,
set_descriptions_verbose=set_descriptions_verbose,
confidence_level=confidence_level,
mean_training_example_dict=mean_training_example_dict,
target_name=scalar_target_names[k],
output_dir_name=output_dir_name)
for k in range(num_aux_targets):
_plot_attributes_diagram(
evaluation_tables_xarray=evaluation_tables_xarray,
line_styles=line_styles,
line_colours=line_colours,
set_descriptions_abbrev=set_descriptions_abbrev,
set_descriptions_verbose=set_descriptions_verbose,
confidence_level=confidence_level,
mean_training_example_dict=mean_training_example_dict,
target_name=aux_target_names[k],
output_dir_name=output_dir_name)
if not plot_by_height:
return
print(SEPARATOR_STRING)
for k in range(num_vector_targets):
for j in range(num_heights):
_plot_attributes_diagram(
evaluation_tables_xarray=evaluation_tables_xarray,
line_styles=line_styles,
line_colours=line_colours,
set_descriptions_abbrev=set_descriptions_abbrev,
set_descriptions_verbose=set_descriptions_verbose,
confidence_level=confidence_level,
mean_training_example_dict=mean_training_example_dict,
height_m_agl=heights_m_agl[j],
target_name=vector_target_names[k],
output_dir_name=output_dir_name)
if k != num_vector_targets - 1:
print(SEPARATOR_STRING)
def _run(model_file_name, example_file_name, num_examples, example_dir_name,
example_id_file_name, layer_name, neuron_indices, ideal_activation,
num_iterations, learning_rate, l2_weight, output_file_name):
"""Runs backwards optimization.
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 layer_name: Same.
:param neuron_indices: Same.
:param ideal_activation: Same.
:param num_iterations: Same.
:param learning_rate: Same.
:param l2_weight: Same.
:param output_file_name: Same.
"""
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, _, example_id_strings = (
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
)
)
print(SEPARATOR_STRING)
generator_option_dict = metadata_dict[neural_net.TRAINING_OPTIONS_KEY]
normalization_file_name = (
generator_option_dict[neural_net.NORMALIZATION_FILE_KEY]
)
print((
'Reading training examples (for normalization) from: "{0:s}"...'
).format(
normalization_file_name
))
training_example_dict = example_io.read_file(normalization_file_name)
training_example_dict = example_utils.subset_by_height(
example_dict=training_example_dict,
heights_m_agl=generator_option_dict[neural_net.HEIGHTS_KEY]
)
num_examples = len(example_id_strings)
bwo_dict = None
for i in range(num_examples):
this_bwo_dict = bwo.optimize_input_for_neuron(
model_object=model_object,
init_function_or_matrix=predictor_matrix[i, ...],
layer_name=layer_name, neuron_indices=neuron_indices,
ideal_activation=ideal_activation, num_iterations=num_iterations,
learning_rate=learning_rate, l2_weight=l2_weight
)
if i == num_examples - 1:
print(SEPARATOR_STRING)
else:
print(MINOR_SEPARATOR_STRING)
if bwo_dict is None:
these_dim = numpy.array(
(num_examples,) +
this_bwo_dict[bwo.INITIAL_PREDICTORS_KEY].shape[1:],
dtype=int
)
bwo_dict = {
bwo.INITIAL_PREDICTORS_KEY: numpy.full(these_dim, numpy.nan),
bwo.FINAL_PREDICTORS_KEY: numpy.full(these_dim, numpy.nan),
bwo.INITIAL_ACTIVATIONS_KEY:
numpy.full(num_examples, numpy.nan),
bwo.FINAL_ACTIVATIONS_KEY: numpy.full(num_examples, numpy.nan)
}
bwo_dict[bwo.INITIAL_PREDICTORS_KEY][i, ...] = (
this_bwo_dict[bwo.INITIAL_PREDICTORS_KEY][0, ...]
)
bwo_dict[bwo.FINAL_PREDICTORS_KEY][i, ...] = (
this_bwo_dict[bwo.FINAL_PREDICTORS_KEY][0, ...]
)
bwo_dict[bwo.INITIAL_ACTIVATIONS_KEY][i] = (
this_bwo_dict[bwo.INITIAL_ACTIVATION_KEY]
)
bwo_dict[bwo.FINAL_ACTIVATIONS_KEY][i] = (
this_bwo_dict[bwo.FINAL_ACTIVATION_KEY]
)
if example_file_name == '':
example_file_name = example_io.find_many_files(
directory_name=example_dir_name,
first_time_unix_sec=0, last_time_unix_sec=int(1e12),
raise_error_if_any_missing=False, raise_error_if_all_missing=True
)[0]
first_example_dict = example_io.read_file(example_file_name)
first_example_dict = example_utils.subset_by_height(
example_dict=first_example_dict,
heights_m_agl=generator_option_dict[neural_net.HEIGHTS_KEY]
)
net_type_string = metadata_dict[neural_net.NET_TYPE_KEY]
init_example_dict = copy.deepcopy(first_example_dict)
this_example_dict = neural_net.predictors_numpy_to_dict(
predictor_matrix=bwo_dict[bwo.INITIAL_PREDICTORS_KEY],
example_dict=init_example_dict, net_type_string=net_type_string
)
init_example_dict.update(this_example_dict)
if generator_option_dict[neural_net.PREDICTOR_NORM_TYPE_KEY] is not None:
init_example_dict = normalization.denormalize_data(
new_example_dict=init_example_dict,
training_example_dict=training_example_dict,
normalization_type_string=
generator_option_dict[neural_net.PREDICTOR_NORM_TYPE_KEY],
min_normalized_value=
generator_option_dict[neural_net.PREDICTOR_MIN_NORM_VALUE_KEY],
max_normalized_value=
generator_option_dict[neural_net.PREDICTOR_MAX_NORM_VALUE_KEY],
separate_heights=True, apply_to_predictors=True,
apply_to_vector_targets=False, apply_to_scalar_targets=False
)
init_scalar_predictor_matrix = (
init_example_dict[example_utils.SCALAR_PREDICTOR_VALS_KEY]
)
init_vector_predictor_matrix = (
init_example_dict[example_utils.VECTOR_PREDICTOR_VALS_KEY]
)
final_example_dict = copy.deepcopy(first_example_dict)
this_example_dict = neural_net.predictors_numpy_to_dict(
predictor_matrix=bwo_dict[bwo.FINAL_PREDICTORS_KEY],
example_dict=final_example_dict, net_type_string=net_type_string
)
final_example_dict.update(this_example_dict)
if generator_option_dict[neural_net.PREDICTOR_NORM_TYPE_KEY] is not None:
final_example_dict = normalization.denormalize_data(
new_example_dict=final_example_dict,
training_example_dict=training_example_dict,
normalization_type_string=
generator_option_dict[neural_net.PREDICTOR_NORM_TYPE_KEY],
min_normalized_value=
generator_option_dict[neural_net.PREDICTOR_MIN_NORM_VALUE_KEY],
max_normalized_value=
generator_option_dict[neural_net.PREDICTOR_MAX_NORM_VALUE_KEY],
separate_heights=True, apply_to_predictors=True,
apply_to_vector_targets=False, apply_to_scalar_targets=False
)
final_scalar_predictor_matrix = (
final_example_dict[example_utils.SCALAR_PREDICTOR_VALS_KEY]
)
final_vector_predictor_matrix = (
final_example_dict[example_utils.VECTOR_PREDICTOR_VALS_KEY]
)
print('Writing results to file: "{0:s}"...'.format(output_file_name))
bwo.write_file(
netcdf_file_name=output_file_name,
init_scalar_predictor_matrix=init_scalar_predictor_matrix,
final_scalar_predictor_matrix=final_scalar_predictor_matrix,
init_vector_predictor_matrix=init_vector_predictor_matrix,
final_vector_predictor_matrix=final_vector_predictor_matrix,
initial_activations=bwo_dict[bwo.INITIAL_ACTIVATIONS_KEY],
final_activations=bwo_dict[bwo.FINAL_ACTIVATIONS_KEY],
example_id_strings=example_id_strings, model_file_name=model_file_name,
layer_name=layer_name, neuron_indices=neuron_indices,
ideal_activation=ideal_activation, num_iterations=num_iterations,
learning_rate=learning_rate, l2_weight=l2_weight
)
def _fluxes_to_heating_rate(vector_target_matrix, vector_prediction_matrix,
model_metadata_dict, prediction_file_name,
example_dir_name):
"""If necessary, converts fluxes to heating rates.
This method is a wrapper for `example_utils.fluxes_to_heating_rate`.
:param vector_target_matrix: See doc for `_fluxes_increments_to_actual`.
:param vector_prediction_matrix: Same.
:param model_metadata_dict: Same.
:param prediction_file_name: See documentation at top of file.
:param example_dir_name: Same.
:return: vector_target_matrix: See doc for `_fluxes_increments_to_actual`.
:return: vector_prediction_matrix: Same.
:return: model_metadata_dict: Same.
"""
generator_option_dict = model_metadata_dict[
neural_net.TRAINING_OPTIONS_KEY]
vector_target_names = (
generator_option_dict[neural_net.VECTOR_TARGET_NAMES_KEY])
need_heating_rate = (example_utils.SHORTWAVE_HEATING_RATE_NAME
not in vector_target_names)
have_fluxes = all([t in vector_target_names for t in FLUX_NAMES])
if not (need_heating_rate and have_fluxes
and example_dir_name is not None):
return (vector_target_matrix, vector_prediction_matrix,
model_metadata_dict)
num_examples = vector_target_matrix.shape[0]
this_example_dict = misc_utils.get_raw_examples(
example_file_name='',
num_examples=0,
example_dir_name=example_dir_name,
example_id_file_name=prediction_file_name)
this_example_dict = example_utils.subset_by_height(
example_dict=this_example_dict,
heights_m_agl=generator_option_dict[neural_net.HEIGHTS_KEY])
pressure_matrix_pascals = example_utils.get_field_from_dict(
example_dict=this_example_dict, field_name=example_utils.PRESSURE_NAME)
pressure_matrix_pascals = pressure_matrix_pascals[:num_examples, ...]
base_example_dict = {
example_utils.SCALAR_PREDICTOR_NAMES_KEY: [],
example_utils.SCALAR_PREDICTOR_VALS_KEY:
numpy.full((num_examples, 0), 0.),
example_utils.VECTOR_PREDICTOR_NAMES_KEY:
[example_utils.PRESSURE_NAME],
example_utils.VECTOR_PREDICTOR_VALS_KEY:
numpy.expand_dims(pressure_matrix_pascals, axis=-1),
example_utils.SCALAR_TARGET_NAMES_KEY: [],
example_utils.SCALAR_TARGET_VALS_KEY:
numpy.full((num_examples, 0), 0.),
example_utils.VECTOR_TARGET_NAMES_KEY:
generator_option_dict[neural_net.VECTOR_TARGET_NAMES_KEY],
example_utils.HEIGHTS_KEY:
generator_option_dict[neural_net.HEIGHTS_KEY],
example_utils.VALID_TIMES_KEY:
numpy.full(num_examples, 0, dtype=int)
}
target_example_dict = copy.deepcopy(base_example_dict)
target_example_dict[example_utils.VECTOR_TARGET_VALS_KEY] = (
vector_target_matrix)
target_example_dict = example_utils.fluxes_to_heating_rate(
target_example_dict)
vector_target_matrix = (
target_example_dict[example_utils.VECTOR_TARGET_VALS_KEY])
prediction_example_dict = copy.deepcopy(base_example_dict)
prediction_example_dict[example_utils.VECTOR_TARGET_VALS_KEY] = (
vector_prediction_matrix)
prediction_example_dict = example_utils.fluxes_to_heating_rate(
prediction_example_dict)
vector_prediction_matrix = (
prediction_example_dict[example_utils.VECTOR_TARGET_VALS_KEY])
generator_option_dict[neural_net.VECTOR_TARGET_NAMES_KEY] = (
target_example_dict[example_utils.VECTOR_TARGET_NAMES_KEY])
model_metadata_dict[neural_net.TRAINING_OPTIONS_KEY] = (
generator_option_dict)
return vector_target_matrix, vector_prediction_matrix, model_metadata_dict
def _run(input_prediction_file_name, average_over_height, scale_by_climo,
num_examples_per_set, output_dir_name):
"""Finds best and worst heating-rate predictions.
This is effectively the main method.
:param input_prediction_file_name: See documentation at top of file.
:param average_over_height: Same.
:param scale_by_climo: Same.
:param num_examples_per_set: Same.
:param output_dir_name: Same.
"""
# TODO(thunderhoser): Maybe allow specific height again (e.g., 15 km).
error_checking.assert_is_greater(num_examples_per_set, 0)
scale_by_climo = scale_by_climo and not average_over_height
print('Reading data from: "{0:s}"...'.format(input_prediction_file_name))
prediction_dict = prediction_io.read_file(input_prediction_file_name)
model_file_name = prediction_dict[prediction_io.MODEL_FILE_KEY]
model_metafile_name = neural_net.find_metafile(
model_dir_name=os.path.split(model_file_name)[0])
print(
'Reading model metadata from: "{0:s}"...'.format(model_metafile_name))
model_metadata_dict = neural_net.read_metafile(model_metafile_name)
generator_option_dict = model_metadata_dict[
neural_net.TRAINING_OPTIONS_KEY]
vector_target_names = (
generator_option_dict[neural_net.VECTOR_TARGET_NAMES_KEY])
hr_index = (vector_target_names.index(
example_utils.SHORTWAVE_HEATING_RATE_NAME))
target_matrix_k_day01 = (
prediction_dict[prediction_io.VECTOR_TARGETS_KEY][..., hr_index])
prediction_matrix_k_day01 = (
prediction_dict[prediction_io.VECTOR_PREDICTIONS_KEY][..., hr_index])
bias_matrix = prediction_matrix_k_day01 - target_matrix_k_day01
absolute_error_matrix = numpy.absolute(bias_matrix)
if average_over_height:
bias_matrix = numpy.mean(bias_matrix, axis=1, keepdims=True)
absolute_error_matrix = numpy.mean(absolute_error_matrix,
axis=1,
keepdims=True)
if scale_by_climo:
normalization_file_name = (
generator_option_dict[neural_net.NORMALIZATION_FILE_KEY])
print(('Reading training examples (for climatology) from: "{0:s}"...'
).format(normalization_file_name))
training_example_dict = example_io.read_file(normalization_file_name)
training_example_dict = example_utils.subset_by_field(
example_dict=training_example_dict,
field_names=[example_utils.SHORTWAVE_HEATING_RATE_NAME])
training_example_dict = example_utils.subset_by_height(
example_dict=training_example_dict,
heights_m_agl=generator_option_dict[neural_net.HEIGHTS_KEY])
dummy_example_dict = {
example_utils.SCALAR_PREDICTOR_NAMES_KEY: [],
example_utils.VECTOR_PREDICTOR_NAMES_KEY: [],
example_utils.SCALAR_TARGET_NAMES_KEY: [],
example_utils.VECTOR_TARGET_NAMES_KEY:
[example_utils.SHORTWAVE_HEATING_RATE_NAME],
example_utils.HEIGHTS_KEY:
generator_option_dict[neural_net.HEIGHTS_KEY]
}
mean_training_example_dict = normalization.create_mean_example(
new_example_dict=dummy_example_dict,
training_example_dict=training_example_dict)
climo_matrix_k_day01 = mean_training_example_dict[
example_utils.VECTOR_TARGET_VALS_KEY][..., 0]
bias_matrix = bias_matrix / climo_matrix_k_day01
absolute_error_matrix = absolute_error_matrix / climo_matrix_k_day01
print(SEPARATOR_STRING)
high_bias_indices, low_bias_indices, low_abs_error_indices = (
misc_utils.find_best_and_worst_predictions(
bias_matrix=bias_matrix,
absolute_error_matrix=absolute_error_matrix,
num_examples_per_set=num_examples_per_set))
print(SEPARATOR_STRING)
high_bias_prediction_dict = prediction_io.subset_by_index(
prediction_dict=copy.deepcopy(prediction_dict),
desired_indices=high_bias_indices)
high_bias_file_name = (
'{0:s}/predictions_high-bias.nc'.format(output_dir_name))
print('Writing examples with greatest positive bias to: "{0:s}"...'.format(
high_bias_file_name))
prediction_io.write_file(
netcdf_file_name=high_bias_file_name,
scalar_target_matrix=high_bias_prediction_dict[
prediction_io.SCALAR_TARGETS_KEY],
vector_target_matrix=high_bias_prediction_dict[
prediction_io.VECTOR_TARGETS_KEY],
scalar_prediction_matrix=high_bias_prediction_dict[
prediction_io.SCALAR_PREDICTIONS_KEY],
vector_prediction_matrix=high_bias_prediction_dict[
prediction_io.VECTOR_PREDICTIONS_KEY],
heights_m_agl=high_bias_prediction_dict[prediction_io.HEIGHTS_KEY],
example_id_strings=high_bias_prediction_dict[
prediction_io.EXAMPLE_IDS_KEY],
model_file_name=high_bias_prediction_dict[
prediction_io.MODEL_FILE_KEY])
low_bias_prediction_dict = prediction_io.subset_by_index(
prediction_dict=copy.deepcopy(prediction_dict),
desired_indices=low_bias_indices)
low_bias_file_name = (
'{0:s}/predictions_low-bias.nc'.format(output_dir_name))
print('Writing examples with greatest negative bias to: "{0:s}"...'.format(
low_bias_file_name))
prediction_io.write_file(
netcdf_file_name=low_bias_file_name,
scalar_target_matrix=low_bias_prediction_dict[
prediction_io.SCALAR_TARGETS_KEY],
vector_target_matrix=low_bias_prediction_dict[
prediction_io.VECTOR_TARGETS_KEY],
scalar_prediction_matrix=low_bias_prediction_dict[
prediction_io.SCALAR_PREDICTIONS_KEY],
vector_prediction_matrix=low_bias_prediction_dict[
prediction_io.VECTOR_PREDICTIONS_KEY],
heights_m_agl=low_bias_prediction_dict[prediction_io.HEIGHTS_KEY],
example_id_strings=low_bias_prediction_dict[
prediction_io.EXAMPLE_IDS_KEY],
model_file_name=low_bias_prediction_dict[prediction_io.MODEL_FILE_KEY])
low_abs_error_prediction_dict = prediction_io.subset_by_index(
prediction_dict=copy.deepcopy(prediction_dict),
desired_indices=low_abs_error_indices)
low_abs_error_file_name = (
'{0:s}/predictions_low-absolute-error.nc'.format(output_dir_name))
print(
'Writing examples with smallest absolute error to: "{0:s}"...'.format(
low_abs_error_file_name))
prediction_io.write_file(
netcdf_file_name=low_abs_error_file_name,
scalar_target_matrix=low_abs_error_prediction_dict[
prediction_io.SCALAR_TARGETS_KEY],
vector_target_matrix=low_abs_error_prediction_dict[
prediction_io.VECTOR_TARGETS_KEY],
scalar_prediction_matrix=low_abs_error_prediction_dict[
prediction_io.SCALAR_PREDICTIONS_KEY],
vector_prediction_matrix=low_abs_error_prediction_dict[
prediction_io.VECTOR_PREDICTIONS_KEY],
heights_m_agl=low_abs_error_prediction_dict[prediction_io.HEIGHTS_KEY],
example_id_strings=low_abs_error_prediction_dict[
prediction_io.EXAMPLE_IDS_KEY],
model_file_name=low_abs_error_prediction_dict[
prediction_io.MODEL_FILE_KEY])
if scale_by_climo:
return
if average_over_height:
mean_targets_k_day01 = numpy.mean(target_matrix_k_day01, axis=1)
sort_indices = numpy.argsort(-1 * mean_targets_k_day01)
else:
max_targets_k_day01 = numpy.max(target_matrix_k_day01, axis=1)
sort_indices = numpy.argsort(-1 * max_targets_k_day01)
large_hr_indices = sort_indices[:num_examples_per_set]
large_hr_prediction_dict = prediction_io.subset_by_index(
prediction_dict=copy.deepcopy(prediction_dict),
desired_indices=large_hr_indices)
large_hr_file_name = (
'{0:s}/predictions_large-heating-rate.nc'.format(output_dir_name))
print('Writing examples with greatest heating rate to: "{0:s}"...'.format(
large_hr_file_name))
prediction_io.write_file(
netcdf_file_name=large_hr_file_name,
scalar_target_matrix=large_hr_prediction_dict[
prediction_io.SCALAR_TARGETS_KEY],
vector_target_matrix=large_hr_prediction_dict[
prediction_io.VECTOR_TARGETS_KEY],
scalar_prediction_matrix=large_hr_prediction_dict[
prediction_io.SCALAR_PREDICTIONS_KEY],
vector_prediction_matrix=large_hr_prediction_dict[
prediction_io.VECTOR_PREDICTIONS_KEY],
heights_m_agl=large_hr_prediction_dict[prediction_io.HEIGHTS_KEY],
example_id_strings=large_hr_prediction_dict[
prediction_io.EXAMPLE_IDS_KEY],
model_file_name=large_hr_prediction_dict[prediction_io.MODEL_FILE_KEY])
if not average_over_height:
return
mean_targets_k_day01 = numpy.mean(target_matrix_k_day01, axis=1)
sort_indices = numpy.argsort(mean_targets_k_day01)
small_hr_indices = sort_indices[:num_examples_per_set]
small_hr_prediction_dict = prediction_io.subset_by_index(
prediction_dict=copy.deepcopy(prediction_dict),
desired_indices=small_hr_indices)
small_hr_file_name = (
'{0:s}/predictions_small-heating-rate.nc'.format(output_dir_name))
print('Writing examples with smallest heating rate to: "{0:s}"...'.format(
small_hr_file_name))
prediction_io.write_file(
netcdf_file_name=small_hr_file_name,
scalar_target_matrix=small_hr_prediction_dict[
prediction_io.SCALAR_TARGETS_KEY],
vector_target_matrix=small_hr_prediction_dict[
prediction_io.VECTOR_TARGETS_KEY],
scalar_prediction_matrix=small_hr_prediction_dict[
prediction_io.SCALAR_PREDICTIONS_KEY],
vector_prediction_matrix=small_hr_prediction_dict[
prediction_io.VECTOR_PREDICTIONS_KEY],
heights_m_agl=small_hr_prediction_dict[prediction_io.HEIGHTS_KEY],
example_id_strings=small_hr_prediction_dict[
prediction_io.EXAMPLE_IDS_KEY],
model_file_name=small_hr_prediction_dict[prediction_io.MODEL_FILE_KEY])
def _run(example_file_name, num_examples, example_dir_name,
example_id_file_name, use_log_scale, model_file_name,
output_dir_name):
"""Plots profiles (vector predictor and target variables) for each example.
This is effectively the main method.
:param example_file_name: See documentation at top of file.
:param num_examples: Same.
:param example_dir_name: Same.
:param example_id_file_name: Same.
:param use_log_scale: Same.
:param model_file_name: Same.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
example_dict = misc_utils.get_raw_examples(
example_file_name=example_file_name,
num_examples=num_examples,
example_dir_name=example_dir_name,
example_id_file_name=example_id_file_name)
num_examples_total = len(example_dict[example_utils.VALID_TIMES_KEY])
if 0 < num_examples < num_examples_total:
desired_indices = numpy.linspace(0,
num_examples - 1,
num=num_examples,
dtype=int)
example_dict = example_utils.subset_by_index(
example_dict=example_dict, desired_indices=desired_indices)
if model_file_name != '':
model_metafile_name = neural_net.find_metafile(
os.path.split(model_file_name)[0])
print('Reading model metadata from: "{0:s}"...\n'.format(
model_metafile_name))
model_metadata_dict = neural_net.read_metafile(model_metafile_name)
generator_option_dict = (
model_metadata_dict[neural_net.TRAINING_OPTIONS_KEY])
vector_predictor_names = (
generator_option_dict[neural_net.VECTOR_PREDICTOR_NAMES_KEY])
all_field_names = vector_predictor_names + VECTOR_TARGET_NAMES
example_dict = example_utils.subset_by_field(
example_dict=example_dict, field_names=all_field_names)
example_dict = example_utils.subset_by_height(
example_dict=example_dict,
heights_m_agl=generator_option_dict[neural_net.HEIGHTS_KEY])
num_examples = len(example_dict[example_utils.VALID_TIMES_KEY])
for i in range(num_examples):
_plot_one_example(example_dict=example_dict,
example_index=i,
use_log_scale=use_log_scale,
output_dir_name=output_dir_name)
print(MINOR_SEPARATOR_STRING)