def _check_training_args(
model_file_name, history_file_name, tensorboard_dir_name, num_epochs,
num_training_batches_per_epoch, num_validation_batches_per_epoch,
training_option_dict, weight_loss_function):
"""Error-checks input arguments for training.
:param model_file_name: Path to output file (HDF5 format). The model will
be saved here after each epoch.
:param history_file_name: Path to output file (CSV format). Training
history (performance metrics) will be saved here after each epoch.
:param tensorboard_dir_name: Path to output directory for TensorBoard log
files.
:param num_epochs: Number of epochs.
:param num_training_batches_per_epoch: Number of training batches in each
epoch.
:param num_validation_batches_per_epoch: Number of validation batches in
each epoch.
:param training_option_dict: See doc for
`training_validation_io.example_generator_2d_or_3d`.
:param weight_loss_function: Boolean flag. If False, classes will be
weighted equally in the loss function. If True, classes will be
weighted differently (inversely proportional to their sampling
fractions).
:return: class_to_weight_dict: Dictionary, where each key is the integer ID
for a target class (-2 for "dead storm") and each value is the weight
for the loss function. If None, classes will be equally weighted in the
loss function.
"""
orig_option_dict = training_option_dict.copy()
training_option_dict = trainval_io.DEFAULT_OPTION_DICT.copy()
training_option_dict.update(orig_option_dict)
file_system_utils.mkdir_recursive_if_necessary(file_name=model_file_name)
file_system_utils.mkdir_recursive_if_necessary(file_name=history_file_name)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=tensorboard_dir_name)
error_checking.assert_is_integer(num_epochs)
error_checking.assert_is_geq(num_epochs, 1)
error_checking.assert_is_integer(num_training_batches_per_epoch)
error_checking.assert_is_geq(num_training_batches_per_epoch, 1)
error_checking.assert_is_integer(num_validation_batches_per_epoch)
error_checking.assert_is_geq(num_validation_batches_per_epoch, 0)
error_checking.assert_is_boolean(weight_loss_function)
if not weight_loss_function:
return None
class_to_sampling_fraction_dict = training_option_dict[
trainval_io.SAMPLING_FRACTIONS_KEY
]
if class_to_sampling_fraction_dict is None:
return None
return dl_utils.class_fractions_to_weights(
sampling_fraction_by_class_dict=class_to_sampling_fraction_dict,
target_name=training_option_dict[trainval_io.TARGET_NAME_KEY],
binarize_target=training_option_dict[trainval_io.BINARIZE_TARGET_KEY]
)
def test_class_fractions_to_weights_wind_7class_binarized(self):
"""Ensures correct output from class_fractions_to_weights.
In this case, target variable = wind-speed class; there are 7 classes;
and binarize_target = True.
"""
this_dict = dl_utils.class_fractions_to_weights(
sampling_fraction_by_class_dict=
SAMPLING_FRACTION_BY_WIND_7CLASS_DICT,
target_name=WIND_TARGET_NAME_7CLASSES,
binarize_target=True)
self.assertTrue(this_dict == LF_WEIGHT_BY_WIND_7CLASS_DICT_BINARIZED)
def test_class_fractions_to_weights_tornado_nonbinarized(self):
"""Ensures correct output from class_fractions_to_weights.
In this case, target variable = tornado occurrence and binarize_target =
False.
"""
this_dict = dl_utils.class_fractions_to_weights(
sampling_fraction_by_class_dict=
SAMPLING_FRACTION_BY_TORNADO_CLASS_DICT,
target_name=TORNADO_TARGET_NAME,
binarize_target=False)
self.assertTrue(this_dict == LF_WEIGHT_BY_TORNADO_CLASS_DICT)
def test_class_fractions_to_weights_tornado_binarized(self):
"""Ensures correct output from class_fractions_to_weights.
In this case, target variable = tornado occurrence and binarize_target =
True.
"""
this_dict = dl_utils.class_fractions_to_weights(
sampling_fraction_by_class_dict=
SAMPLING_FRACTION_BY_TORNADO_CLASS_DICT,
target_name=TORNADO_TARGET_NAME,
binarize_target=True)
expected_keys = list(LF_WEIGHT_BY_TORNADO_CLASS_DICT.keys())
actual_keys = list(this_dict.keys())
self.assertTrue(set(expected_keys) == set(actual_keys))
for this_key in expected_keys:
self.assertTrue(
numpy.isclose(LF_WEIGHT_BY_TORNADO_CLASS_DICT[this_key],
this_dict[this_key],
atol=TOLERANCE_FOR_CLASS_WEIGHT))