def fit(self, source_df, y, experiment: ExperimentBackend) -> pd.DataFrame:
X, y = source_df.values, y
default_params = self.generate_default_model_parameter(
X, y, experiment)
with experiment.mark_time(prefix='fit'):
models, oof = self.run_oof_train(X,
y,
default_params,
experiment=experiment)
self._fitted_models = models
experiment.mark('n_cv', len(models))
return pd.DataFrame(oof)
def frozen(self, experiment: ExperimentBackend) -> 'MetaBlock':
"""
save fitted models to the experiment
Args:
experiment:
保存する対象となる environment
Returns:
myself
"""
if not self._check_has_fitted_models():
raise NotFittedError()
dir_names = [
self._get_fold_dir(i) for i in range(len(self._fitted_models))
]
for name, model in zip(dir_names, self._fitted_models):
with experiment.as_environment(name, style='nested') as fold_env:
fold_env.save_as_python_object('model', model)
experiment.mark('cv_dirs', dir_names)
return self