def before_train(self, logs=None):
"""Call before_train of the managed callbacks."""
super().before_train(logs)
"""Be called before the training process."""
hpo_result = FileOps.load_pickle(
FileOps.join_path(self.trainer.local_output_path,
'best_config.pickle'))
logging.info("loading stage1_hpo_result \n{}".format(hpo_result))
feature_interaction_score = hpo_result['feature_interaction_score']
print('feature_interaction_score:', feature_interaction_score)
sorted_pairs = sorted(feature_interaction_score.items(),
key=lambda x: abs(x[1]),
reverse=True)
model_cfg = ClassFactory.__configs__.get('model')
if model_cfg:
fis_ratio = model_cfg["model_desc"]["custom"]["fis_ratio"]
else:
fis_ratio = 1.0
top_k = int(len(feature_interaction_score) * min(1.0, fis_ratio))
self.selected_pairs = list(map(lambda x: x[0], sorted_pairs[:top_k]))
# add selected_pairs
setattr(model_cfg["model_desc"]["custom"], 'selected_pairs',
self.selected_pairs)
def before_train(self, logs=None):
"""Call before_train of the managed callbacks."""
super().before_train(logs)
"""Be called before the training process."""
hpo_result = FileOps.load_pickle(
FileOps.join_path(self.trainer.local_output_path,
'best_config.pickle'))
logging.info("loading stage1_hpo_result \n{}".format(hpo_result))
self.selected_pairs = hpo_result['feature_interaction']
print('feature_interaction:', self.selected_pairs)
model_cfg = ClassFactory.__configs__.get('model')
# add selected_pairs
setattr(model_cfg["model_desc"]["custom"], 'selected_pairs',
self.selected_pairs)
def update(self, record):
"""Update sampler."""
step_name = record.get("step_name")
worker_id = record.get("worker_id")
worker_result_path = TaskOps().get_local_worker_path(
step_name, worker_id)
performance_file = self.performance_path(worker_result_path)
logging.info(
"SpNas.update(), performance file={}".format(performance_file))
info = FileOps.load_pickle(performance_file)
if info is not None:
self._total_list.append(info)
else:
logging.info("SpNas.update(), file is not exited, "
"performance file={}".format(performance_file))
self.save_output(self.output_path)
if self.backup_base_path is not None:
FileOps.copy_folder(self.output_path, self.backup_base_path)
