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callbacks.py
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callbacks.py
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"""
Neuraxle's training callbacks classes.
=========================================
Training callback classes.
..
Copyright 2019, Neuraxio Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
..
Thanks to Umaneo Technologies Inc. for their contributions to this Machine Learning
project, visit https://www.umaneo.com/ for more information on Umaneo Technologies Inc.
"""
import traceback
from abc import ABC, abstractmethod
from typing import Callable
from neuraxle.data_container import DataContainer
from neuraxle.metaopt.trial import TrialSplit
class BaseCallback(ABC):
"""
Base class for a training callback.
Callbacks are called after each epoch inside the fit function of the :class:`~neuraxle.metaopt.automl.Trainer`.
.. seealso::
:class:`MetaCallback`,
:class:`EarlyStoppingCallback`,
:class:`IfBestScore`,
:class:`IfLastStep`,
:class:`StepSaverCallback`,
:class:`~neuraxle.metaopt.auto_ml.AutoML`,
:class:`~neuraxle.metaopt.auto_ml.Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`~neuraxle.metaopt.auto_ml.InMemoryHyperparamsRepository`,
:class:`~neuraxle.metaopt.auto_ml.HyperparamsJSONRepository`,
:class:`~neuraxle.metaopt.auto_ml.BaseHyperparameterSelectionStrategy`,
:class:`~neuraxle.metaopt.auto_ml.RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.base.HyperparameterSamples`,
:class:`~neuraxle.data_container.DataContainer`
"""
@abstractmethod
def call(self, trial: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
is_finished_and_fitted: bool):
pass
class EarlyStoppingCallback(BaseCallback):
"""
Perform early stopping when there is multiple epochs in a row that didn't improve the performance of the model.
.. seealso::
:class:`BaseCallback`,
:class:`MetaCallback`,
:class:`IfBestScore`,
:class:`IfLastStep`,
:class:`StepSaverCallback`,
:class:`~neuraxle.metaopt.auto_ml.AutoML`,
:class:`~neuraxle.metaopt.auto_ml.Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`~neuraxle.metaopt.auto_ml.InMemoryHyperparamsRepository`,
:class:`~neuraxle.metaopt.auto_ml.HyperparamsJSONRepository`,
:class:`~neuraxle.metaopt.auto_ml.BaseHyperparameterSelectionStrategy`,
:class:`~neuraxle.metaopt.auto_ml.RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.base.HyperparameterSamples`,
:class:`~neuraxle.data_container.DataContainer`
"""
def __init__(self, max_epochs_without_improvement):
self.n_epochs_without_improvement = max_epochs_without_improvement
def call(
self,
trial: TrialSplit,
epoch_number: int,
total_epochs: int,
input_train: DataContainer,
pred_train: DataContainer,
input_val: DataContainer,
pred_val: DataContainer,
is_finished_and_fitted: bool
):
validation_scores = trial.get_validation_scores()
if len(validation_scores) > self.n_epochs_without_improvement:
higher_score_is_better = trial.is_higher_score_better()
if validation_scores[-self.n_epochs_without_improvement] >= validation_scores[
-1] and higher_score_is_better:
return True
if validation_scores[-self.n_epochs_without_improvement] <= validation_scores[
-1] and not higher_score_is_better:
return True
return False
class MetaCallback(BaseCallback):
"""
Meta callback wraps another callback.
It can be useful to test conditions before executing certain callbacks.
.. seealso::
:class:`BaseCallback`,
:class:`IfBestScore`,
:class:`IfLastStep`,
:class:`StepSaverCallback`,
:class:`~neuraxle.metaopt.auto_ml.AutoML`,
:class:`~neuraxle.metaopt.auto_ml.Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`~neuraxle.metaopt.auto_ml.InMemoryHyperparamsRepository`,
:class:`~neuraxle.metaopt.auto_ml.HyperparamsJSONRepository`,
:class:`~neuraxle.metaopt.auto_ml.BaseHyperparameterSelectionStrategy`,
:class:`~neuraxle.metaopt.auto_ml.RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.base.HyperparameterSamples`,
:class:`~neuraxle.data_container.DataContainer`
"""
def __init__(self, wrapped_callback: BaseCallback):
self.wrapped_callback = wrapped_callback
@abstractmethod
def call(
self,
trial: TrialSplit,
epoch_number: int,
total_epochs: int,
input_train: DataContainer,
pred_train: DataContainer,
input_val: DataContainer,
pred_val: DataContainer,
is_finished_and_fitted: bool
):
pass
class IfBestScore(MetaCallback):
"""
Meta callback that only execute when the trial is a new best score.
.. seealso::
:class:`BaseCallback`,
:class:`MetaCallback`,
:class:`IfBestScore`,
:class:`IfLastStep`,
:class:`StepSaverCallback`,
:class:`~neuraxle.metaopt.auto_ml.AutoML`,
:class:`~neuraxle.metaopt.auto_ml.Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`~neuraxle.metaopt.auto_ml.InMemoryHyperparamsRepository`,
:class:`~neuraxle.metaopt.auto_ml.HyperparamsJSONRepository`,
:class:`~neuraxle.metaopt.auto_ml.BaseHyperparameterSelectionStrategy`,
:class:`~neuraxle.metaopt.auto_ml.RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.base.HyperparameterSamples`,
:class:`~neuraxle.data_container.DataContainer`
"""
def call(self, trial: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
is_finished_and_fitted: bool):
if trial.is_new_best_score():
if self.wrapped_callback.call(
trial,
epoch_number,
total_epochs,
input_train,
pred_train,
input_val,
pred_val,
is_finished_and_fitted
):
return True
return False
class IfLastStep(MetaCallback):
"""
Meta callback that only execute when the training is finished or fitted, or when it is the last epoch.
.. seealso::
:class:`BaseCallback`,
:class:`MetaCallback`,
:class:`IfBestScore`,
:class:`StepSaverCallback`,
:class:`~neuraxle.metaopt.auto_ml.AutoML`,
:class:`~neuraxle.metaopt.auto_ml.Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`~neuraxle.metaopt.auto_ml.InMemoryHyperparamsRepository`,
:class:`~neuraxle.metaopt.auto_ml.HyperparamsJSONRepository`,
:class:`~neuraxle.metaopt.auto_ml.BaseHyperparameterSelectionStrategy`,
:class:`~neuraxle.metaopt.auto_ml.RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.base.HyperparameterSamples`,
:class:`~neuraxle.data_container.DataContainer`
"""
def call(self, trial: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
is_finished_and_fitted: bool):
if epoch_number == total_epochs - 1 or is_finished_and_fitted:
self.wrapped_callback.call(
trial,
epoch_number,
total_epochs,
input_train,
pred_train,
input_val,
pred_val,
is_finished_and_fitted
)
return True
return False
class StepSaverCallback(BaseCallback):
"""
Callback that saves the trial model.
.. seealso::
:class:`BaseCallback`,
:class:`MetaCallback`,
:class:`EarlyStoppingCallback`,
:class:`IfBestScore`,
:class:`IfLastStep`,
:class:`StepSaverCallback`,
:class:`~neuraxle.metaopt.auto_ml.AutoML`,
:class:`~neuraxle.metaopt.auto_ml.Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`~neuraxle.metaopt.auto_ml.InMemoryHyperparamsRepository`,
:class:`~neuraxle.metaopt.auto_ml.HyperparamsJSONRepository`,
:class:`~neuraxle.metaopt.auto_ml.BaseHyperparameterSelectionStrategy`,
:class:`~neuraxle.metaopt.auto_ml.RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.base.HyperparameterSamples`,
:class:`~neuraxle.data_container.DataContainer`
"""
def call(self, trial: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
is_finished_and_fitted: bool):
trial.save_model()
return False
class CallbackList(BaseCallback):
"""
Callback list that be executed.
.. seealso::
:class:`BaseCallback`,
:class:`MetaCallback`,
:class:`EarlyStoppingCallback`,
:class:`IfBestScore`,
:class:`IfLastStep`,
:class:`StepSaverCallback`,
:class:`~neuraxle.metaopt.auto_ml.AutoML`,
:class:`~neuraxle.metaopt.auto_ml.Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`~neuraxle.metaopt.auto_ml.InMemoryHyperparamsRepository`,
:class:`~neuraxle.metaopt.auto_ml.HyperparamsJSONRepository`,
:class:`~neuraxle.metaopt.auto_ml.BaseHyperparameterSelectionStrategy`,
:class:`~neuraxle.metaopt.auto_ml.RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.base.HyperparameterSamples`,
:class:`~neuraxle.data_container.DataContainer`
"""
def __init__(self, callbacks, print_func: Callable = None):
self.callbacks = callbacks
if print_func is None:
print_func = print
self.print_func = print_func
def __getitem__(self, item):
return self.callbacks[item]
def call(self, trial: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
is_finished_and_fitted: bool):
is_finished_and_fitted = False
for callback in self.callbacks:
try:
if callback.call(
trial=trial,
epoch_number=epoch_number,
total_epochs=total_epochs,
input_train=input_train,
pred_train=pred_train,
input_val=input_val,
pred_val=pred_val,
is_finished_and_fitted=is_finished_and_fitted
):
is_finished_and_fitted = True
except Exception as error:
track = traceback.format_exc()
self.print_func(track)
return is_finished_and_fitted
class MetricCallback(BaseCallback):
"""
Callback that calculates metric results.
Adds the results into the trial repository.
.. seealso::
:class:`BaseCallback`,
:class:`MetaCallback`,
:class:`EarlyStoppingCallback`,
:class:`IfBestScore`,
:class:`IfLastStep`,
:class:`StepSaverCallback`,
:class:`CallbackList`,
:class:`~neuraxle.metaopt.auto_ml.AutoML`,
:class:`~neuraxle.metaopt.auto_ml.Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`~neuraxle.metaopt.auto_ml.InMemoryHyperparamsRepository`,
:class:`~neuraxle.metaopt.auto_ml.HyperparamsJSONRepository`,
:class:`~neuraxle.metaopt.auto_ml.BaseHyperparameterSelectionStrategy`,
:class:`~neuraxle.metaopt.auto_ml.RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.base.HyperparameterSamples`,
:class:`~neuraxle.data_container.DataContainer`
"""
def __init__(self, name: str, metric_function: Callable, higher_score_is_better: bool, print_metrics=True,
print_function=None):
self.name = name
self.metric_function = metric_function
self.higher_score_is_better = higher_score_is_better
self.print_metrics = print_metrics
if print_function is None:
print_function = print
self.print_function = print_function
def call(self, trial: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
is_finished_and_fitted: bool):
train_score = self.metric_function(pred_train.expected_outputs, pred_train.data_inputs)
validation_score = self.metric_function(pred_val.expected_outputs, pred_val.data_inputs)
trial.add_metric_results_train(
name=self.name,
score=train_score,
higher_score_is_better=self.higher_score_is_better
)
trial.add_metric_results_validation(
name=self.name,
score=validation_score,
higher_score_is_better=self.higher_score_is_better
)
if self.print_metrics:
self.print_function('{} train: {}'.format(self.name, train_score))
self.print_function('{} validation: {}'.format(self.name, validation_score))
return False
class ScoringCallback(MetricCallback):
"""
Metric Callback that calculates metric results for the main scoring metric.
Adds the results into the trial repository.
.. seealso::
:class:`BaseCallback`,
:class:`MetaCallback`,
:class:`EarlyStoppingCallback`,
:class:`IfBestScore`,
:class:`IfLastStep`,
:class:`StepSaverCallback`,
:class:`CallbackList`,
:class:`~neuraxle.metaopt.auto_ml.AutoML`,
:class:`~neuraxle.metaopt.auto_ml.Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`~neuraxle.metaopt.auto_ml.InMemoryHyperparamsRepository`,
:class:`~neuraxle.metaopt.auto_ml.HyperparamsJSONRepository`,
:class:`~neuraxle.metaopt.auto_ml.BaseHyperparameterSelectionStrategy`,
:class:`~neuraxle.metaopt.auto_ml.RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.base.HyperparameterSamples`,
:class:`~neuraxle.data_container.DataContainer`
"""
def __init__(self, metric_function: Callable, name='main', higher_score_is_better: bool = True, print_metrics: bool = True):
super().__init__(
name=name,
metric_function=metric_function,
higher_score_is_better=higher_score_is_better,
print_metrics=print_metrics
)