Source code for gemseo.problems.scalable.data_driven.factory
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or
# initial documentation
# :author: Matthias De Lozzo
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""Scalable model factory.
This module contains the :class:`.ScalableModelFactory` which is a factory
to create a :class:`.ScalableModel` from its class name by means of the
:meth:`.ScalableModelFactory.create` method. It is also possible to get a list
of available scalable models
(see :attr:`.ScalableModelFactory.scalable_models` method)
and to check is a type of scalable model is available
(see :meth:`.ScalableModelFactory.is_available` method)
"""
from __future__ import annotations
from typing import TYPE_CHECKING
from typing import Any
from gemseo.core.base_factory import BaseFactory
from gemseo.problems.scalable.data_driven.model import ScalableModel
if TYPE_CHECKING:
from collections.abc import Mapping
from gemseo.datasets.io_dataset import IODataset
[docs]
class ScalableModelFactory(BaseFactory):
"""This factory instantiates a class:`.ScalableModel` from its class name.
The class can be internal to |g| or located in an external module whose path is
provided to the constructor.
"""
_CLASS = ScalableModel
_MODULE_NAMES = ("gemseo.problems.scalable",)
[docs]
def create(
self,
model_name: str,
data: IODataset,
sizes: Mapping[str, int] | None = None,
**parameters: Any,
) -> ScalableModel:
"""Create a scalable model.
Args:
model_name: The name of the scalable model (its class name).
data: The input-output dataset.
sizes: The sizes of the inputs and outputs.
If ``None``, use the original sizes.
**parameters: model parameters
Returns:
The scalable model.
"""
return super().create(model_name, data=data, sizes=sizes, **parameters)
@property
def scalable_models(self) -> list[str]:
"""The available scalable models."""
return self.class_names