def __init__(
self,
epochs: int,
scoring_callback: ScoringCallback,
validation_splitter: 'BaseValidationSplitter',
callbacks: List[BaseCallback] = None,
print_func: Callable = None,
hyperparams_repository: HyperparamsRepository = None
):
self.epochs: int = epochs
self.validation_split_function = validation_splitter
if callbacks is None:
callbacks = []
callbacks: List[BaseCallback] = [scoring_callback] + callbacks
self.callbacks: CallbackList = CallbackList(callbacks)
if print_func is None:
print_func = print
if hyperparams_repository is None:
hyperparams_repository = InMemoryHyperparamsRepository()
self.hyperparams_repository: HyperparamsRepository = hyperparams_repository
self.print_func = print_func
def __init__(self,
epochs,
metrics=None,
callbacks=None,
print_metrics=True,
print_func=None):
self.epochs = epochs
if metrics is None:
metrics = {}
self.metrics = metrics
self._initialize_metrics(metrics)
self.callbacks = CallbackList(callbacks)
if print_func is None:
print_func = print
self.print_func = print_func
self.print_metrics = print_metrics
def __init__(
self,
epochs: int,
scoring_callback: ScoringCallback,
validation_splitter: 'BaseValidationSplitter',
callbacks: List[BaseCallback] = None,
print_func: Callable = None
):
self.epochs: int = epochs
self.validation_split_function = validation_splitter
if callbacks is None:
callbacks = []
callbacks: List[BaseCallback] = [scoring_callback] + callbacks
self.callbacks: CallbackList = CallbackList(callbacks)
if print_func is None:
print_func = print
self.print_func = print_func
class Trainer:
"""
Example usage :
.. code-block:: python
trainer = Trainer(
callbacks=[],
epochs=10,
print_func=print
)
repo_trial = trainer.fit(
p=p,
trial_repository=repo_trial,
train_data_container=training_data_container,
validation_data_container=validation_data_container,
context=context
)
pipeline = trainer.refit(repo_trial.pipeline, data_container, context)
.. seealso::
:class:`AutoML`,
:class:`Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`InMemoryHyperparamsRepository`,
:class:`HyperparamsJSONRepository`,
:class:`BaseHyperparameterSelectionStrategy`,
:class:`RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.hyperparams.space.HyperparameterSamples`
"""
def __init__(self,
epochs,
metrics=None,
callbacks=None,
print_metrics=True,
print_func=None):
self.epochs = epochs
if metrics is None:
metrics = {}
self.metrics = metrics
self._initialize_metrics(metrics)
self.callbacks = CallbackList(callbacks)
if print_func is None:
print_func = print
self.print_func = print_func
self.print_metrics = print_metrics
def fit_trial_split(self, trial_split: TrialSplit,
train_data_container: DataContainer,
validation_data_container: DataContainer,
context: ExecutionContext) -> TrialSplit:
"""
Train pipeline using the training data container.
Track training, and validation metrics for each epoch.
:param train_data_container: train data container
:param validation_data_container: validation data container
:param trial_split: trial to execute
:param context: execution context
:return: executed trial
"""
early_stopping = False
for i in range(self.epochs):
self.print_func('\nepoch {}/{}'.format(i + 1, self.epochs))
trial_split = trial_split.fit_trial_split(train_data_container,
context)
y_pred_train = trial_split.predict_with_pipeline(
train_data_container, context)
y_pred_val = trial_split.predict_with_pipeline(
validation_data_container, context)
if self.callbacks.call(trial=trial_split,
epoch_number=i,
total_epochs=self.epochs,
input_train=train_data_container,
pred_train=y_pred_train,
input_val=validation_data_container,
pred_val=y_pred_val,
is_finished_and_fitted=early_stopping):
break
return trial_split
def refit(self, p: BaseStep, data_container: DataContainer,
context: ExecutionContext) -> BaseStep:
"""
Refit the pipeline on the whole dataset (without any validation technique).
:param p: trial to refit
:param data_container: data container
:param context: execution context
:return: fitted pipeline
"""
for i in range(self.epochs):
p = p.handle_fit(data_container, context)
return p
def _initialize_metrics(self, metrics):
"""
Initialize metrics results dict for train, and validation using the metrics function dict.
:param metrics: metrics function dict
:return:
"""
self.metrics_results_train = {}
self.metrics_results_validation = {}
for m in metrics:
self.metrics_results_train[m] = []
self.metrics_results_validation[m] = []
def get_main_metric_name(self) -> str:
"""
Get main metric name.
:return:
"""
return self.callbacks[0].name