def final_fit(self, **kwargs):
best_trial = self.oracle.get_best_trials(1)[0]
best_hp = best_trial.hyperparameters
pipeline, kwargs["x"], kwargs[
"validation_data"] = self._prepare_model_build(best_hp, **kwargs)
model = self._build_best_model()
self.adapt(model, kwargs["x"])
model, _ = utils.fit_with_adaptive_batch_size(
model, self.hypermodel.hypermodel.batch_size, **kwargs)
return pipeline, model
def _build_and_fit_model(self, trial, fit_args, fit_kwargs):
(
pipeline,
fit_kwargs["x"],
fit_kwargs["validation_data"],
) = self._prepare_model_build(trial.hyperparameters, **fit_kwargs)
pipeline.save(self._pipeline_path(trial.trial_id))
model = self.hypermodel.build(trial.hyperparameters)
self.adapt(model, fit_kwargs["x"])
_, history = utils.fit_with_adaptive_batch_size(
model, self.hypermodel.hypermodel.batch_size, **fit_kwargs)
return history
