Source code for gemseo_mlearning.api

# 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.
"""Some useful functions for machine learning."""
from __future__ import annotations

from typing import Any
from typing import Iterable
from typing import Mapping
from typing import Sequence

from gemseo.algos.design_space import DesignSpace
from gemseo.algos.doe.doe_lib import DOELibrary
from gemseo.algos.doe.doe_lib import DOELibraryOptionType
from gemseo.api import create_scenario
from gemseo.core.dataset import Dataset
from gemseo.core.discipline import MDODiscipline
from gemseo.core.scenario import Scenario


[docs]def sample_discipline( discipline: MDODiscipline, input_space: DesignSpace, output_names: str | Iterable[str], algo_name: str, n_samples: int, name: str = None, **algo_options: Any, ) -> Dataset: """Sample a discipline. Args: discipline: The discipline to be sampled. input_space: The input space on which to sample the discipline. output_names: The names of the outputs of interest. algo_name: The name of the DOE algorithm. n_samples: The number of samples. name: The name of the returned dataset. If ``None``, use the name of the discipline. **algo_options: The options of the DOE algorithm. Returns: The input-output samples of the disciplines. """ return sample_disciplines( [discipline], "DisciplinaryOpt", input_space, output_names, algo_name, n_samples, name or discipline.name, **algo_options, )
[docs]def sample_disciplines( disciplines: Sequence[MDODiscipline], formulation: str, input_space: DesignSpace, output_names: str | Iterable[str], algo_name: str, n_samples: int, name: str = None, formulation_options: Mapping[str, Any] = None, **algo_options: DOELibraryOptionType, ) -> Dataset: """Sample several disciplines based on an MDO formulation. Args: disciplines: The disciplines to be sampled. formulation: The name of the MDO formulation. input_space: The input space on which to sample the discipline. output_names: The names of the outputs of interest. algo_name: The name of the DOE algorithm. n_samples: The number of samples. name: The name of the returned dataset. If ``None``, use the name of the discipline. formulation_options: The options of the MDO formulation. If ``None``, use the default ones. **algo_options: The options of the DOE algorithm. Returns: The input-output samples of the disciplines. """ if isinstance(output_names, str): output_names = [output_names] formulation_options = formulation_options or {} output_names_iterator = iter(output_names) scenario = create_scenario( disciplines, formulation, next(output_names_iterator), input_space, scenario_type="DOE", **formulation_options, ) for output_name in output_names_iterator: scenario.add_observable(output_name) scenario.execute( { Scenario.ALGO: algo_name, DOELibrary.N_SAMPLES: n_samples, Scenario.ALGO_OPTIONS: algo_options, } ) return scenario.formulation.opt_problem.export_to_dataset( name=name, opt_naming=False )