def on_overwatch_metric_computed(
self, current_overwatch_metric: OverWatchMetric):
"""
AUTHORS:
--------
:author: Alix Leroy
:author: Samuel Westlake
DESCRIPTION:
------------
Check if saving the model is required
PARAMETERS:
-----------
:param current_overwatch_metric: float: The value of the metric to over watch
RETURN:
-------
:return -> bool: Whether the model should be saved or not
"""
# Save if there is no metric to compare against
if self.best_overwatch_metric is None:
self.best_overwatch_metric = current_overwatch_metric
save = True
else:
# If the new metric has to be smaller than the best one
if DEEP_SAVE_CONDITION_LESS.corresponds(
current_overwatch_metric.get_condition()):
# If the model improved since last batch => Save
if self.best_overwatch_metric.get_value(
) > current_overwatch_metric.get_value():
Notification(
DEEP_NOTIF_SUCCESS, DEEP_MSG_SAVER_IMPROVED %
(current_overwatch_metric.name, "%.4e" %
Decimal(self.best_overwatch_metric.get_value() -
current_overwatch_metric.get_value())))
self.best_overwatch_metric = current_overwatch_metric
save = True
# No improvement => Return False
else:
Notification(
DEEP_NOTIF_INFO, DEEP_MSG_SAVER_NOT_IMPROVED %
current_overwatch_metric.name)
save = False
# If the new metric has to be bigger than the best one (e.g. The accuracy of a classification)
elif DEEP_SAVE_CONDITION_GREATER.corresponds(
current_overwatch_metric.get_condition()):
# If the model improved since last batch => Save
if self.best_overwatch_metric.get_value(
) < current_overwatch_metric.get_value():
Notification(
DEEP_NOTIF_SUCCESS, DEEP_MSG_SAVER_IMPROVED %
(current_overwatch_metric.name, "%.4e" %
Decimal(current_overwatch_metric.get_value() -
self.best_overwatch_metric.get_value())))
self.best_overwatch_metric = current_overwatch_metric
save = True
# No improvement => Return False
else:
Notification(
DEEP_NOTIF_INFO, DEEP_MSG_SAVER_NOT_IMPROVED %
current_overwatch_metric.name)
save = False
else:
Notification(
DEEP_NOTIF_FATAL,
"The following saving condition does not exist : %s" %
current_overwatch_metric.get_condition())
save = False
if save is True:
self.save_model()
def is_saving_required(self,
current_overwatch_metric: OverWatchMetric) -> bool:
"""
AUTHORS:
--------
:author: Alix Leroy
DESCRIPTION:
------------
Check if saving the model is required
PARAMETERS:
-----------
:param current_overwatch_metric_value->float: The value of the metric to over watch
RETURN:
-------
:return->bool: Whether the model should be saved or not
"""
save = False
# Do not save at the first epoch
if self.best_overwatch_metric is None:
self.best_overwatch_metric = current_overwatch_metric
save = False
# If the new metric has to be smaller than the best one
if current_overwatch_metric.get_condition() == DEEP_COMPARE_SMALLER:
# If the model improved since last batch => Save
if self.best_overwatch_metric.get_value(
) > current_overwatch_metric.get_value():
self.best_overwatch_metric = current_overwatch_metric
save = True
# No improvement => Return False
else:
save = False
# If the new metric has to be bigger than the best one (e.g. The accuracy of a classification)
elif current_overwatch_metric.get_condition() == DEEP_COMPARE_BIGGER:
# If the model improved since last batch => Save
if self.best_overwatch_metric.get_value(
) < current_overwatch_metric.get_value():
self.best_overwatch_metric = current_overwatch_metric
save = True
# No improvement => Return False
else:
save = False
else:
Notification(
DEEP_NOTIF_FATAL,
"The following saving condition does not exist : " +
str("test"))
Thalamus().add_signal(signal=Signal(event=DEEP_EVENT_SAVING_REQUIRED,
args={"saving_required": save}))