def _default_callbacks(self):
return [
('epoch_timer', EpochTimer()),
('train_loss',
BatchScoring(
train_loss_score,
name='train_loss',
on_train=True,
target_extractor=noop,
)),
('valid_loss',
BatchScoring(
valid_loss_score,
name='valid_loss',
target_extractor=noop,
)),
('print_log', PrintLog()),
]
def callbacks_monkey_patch(self):
return [
('epoch_timer', EpochTimer()),
('train_loss', BatchScoring(
train_loss_score,
name='train_loss',
on_train=True,
target_extractor= \
lambda x: self.encoder.transform(np.array(x).reshape(-1, 1)),
)),
('valid_loss', BatchScoring(
valid_loss_score,
name='valid_loss',
target_extractor= \
lambda x: self.encoder.transform(np.array(x).reshape(-1, 1)),
)),
('print_log', PrintLog()),
]
def final_fit(self, dataset, best_params=None, train_all=False):
if best_params is None:
best_params = self.model.get_params()
# Once the best parameters are found, the best model is trained on the whole training set.
if not self.model.train_split is None and isinstance(self.model.train_split, CVSplit):
best_params.update({"train_split": None if train_all else CVSplit(5)})
# Callbacks
train_acc = BatchScoring(scoring='accuracy', on_train=True,
name='train_acc', lower_is_better=False)
# Callbacks
valid_acc = BatchScoring(scoring='accuracy', on_train=False,
name='valid_acc', lower_is_better=False)
best_params.update({"callbacks": [train_acc, valid_acc]})
self.best_model.set_params(**best_params)
self.best_model.fit(dataset, None)
# saving
with open(path.join(ARTIFACTS_DIR, self.name + '.pkl'), 'wb') as f:
pickle.dump(self, f)
def _default_callbacks(self):
default_cb_list = [
('epoch_timer', EpochTimer()),
('train_loss',
BatchScoring(train_loss_score,
name='train_loss',
on_train=True,
target_extractor=noop)),
('valid_loss',
BatchScoring(valid_loss_score,
name='valid_loss',
target_extractor=noop)),
('valid_acc',
EpochScoring(
'accuracy',
name='valid_acc',
lower_is_better=False,
)),
# ('checkpoint', Checkpoint(
# dirname=self.model_path)),
# ('end_checkpoint', TrainEndCheckpoint(
# dirname=self.model_path)),
('report', ReportLog()),
('progressbar', ProgressBar())
]
# if 'stop_patience' in self.hyperparamters.keys() and \
# self.hyperparamters['stop_patience']:
# earlystop_cb = ('earlystop', EarlyStopping(
# patience=self.patience,
# threshold=1e-4))
# default_cb_list.append(earlystop_cb)
#
# if 'lr_step' in self.hyperparamters.keys() and \
# self.hyperparamters['lr_step']:
# lr_callback = ('lr_schedule', DecayLR(
# self.hyperparamters['lr'],
# self.hyperparamters['lr_step'],
# gamma=0.5))
# default_cb_list.append(lr_callback)
return default_cb_list
def _default_callbacks(self):
return [
("epoch_timer", EpochTimer()),
(
"train_loss",
BatchScoring(
train_loss_score,
name="train_loss",
on_train=True,
target_extractor=noop,
),
),
(
"valid_loss",
BatchScoring(
valid_loss_score, name="valid_loss", target_extractor=noop,
),
),
("print_log", PrintLog()),
]