Source code for gemseo.core.doe_scenario

# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry,
# 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
# 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: Francois Gallard
"""A scenario whose driver is a design of experiments."""
from __future__ import division, unicode_literals

import logging
from typing import Any, Optional, Sequence

from gemseo.algos.design_space import DesignSpace
from gemseo.algos.doe.doe_factory import DOEFactory
from gemseo.core.discipline import MDODiscipline
from gemseo.core.scenario import Scenario

# The detection of formulations requires to import them,
# before calling get_formulation_from_name
LOGGER = logging.getLogger(__name__)

[docs]class DOEScenario(Scenario): """A multidisciplinary scenario to be executed by a design of experiments (DOE). A :class:`DOEScenario` is a particular :class:`.Scenario` whose driver is a DOE. This DOE must be implemented in a :class:`.DOELibrary`. Attributes: seed (int): The seed used by the random number generators for replicability. """ # Constants for input variables in json schema N_SAMPLES = "n_samples" EVAL_JAC = "eval_jac" SEED = "seed" def __init__( self, disciplines, # type: Sequence[MDODiscipline] formulation, # type: str objective_name, # type: str design_space, # type: DesignSpace name=None, # type: Optional[str] grammar_type=MDODiscipline.JSON_GRAMMAR_TYPE, # type: str **formulation_options # type: Any ): # type: (...) -> None """ Args: disciplines: The disciplines used to compute the objective, constraints and observables from the design variables. formulation: The name of the MDO formulation, also the name of a class inheriting from :class:`.MDOFormulation`. objective_name: The name of the objective. design_space: The design space. name: The name to be given to this scenario. If None, use the name of the class. grammar_type: The type of grammar to use for IO declaration , e.g. JSON_GRAMMAR_TYPE or SIMPLE_GRAMMAR_TYPE. **formulation_options: The options to be passed to the :class:`.MDOFormulation`. """ # This loads the right json grammars from class name super(DOEScenario, self).__init__( disciplines, formulation, objective_name, design_space, name, grammar_type, **formulation_options ) self.seed = 0 self.default_inputs = {self.EVAL_JAC: False, self.ALGO: "lhs"} def _init_algo_factory(self): # type: (...) -> None self._algo_factory = DOEFactory() def _run_algorithm(self): # type: (...) -> None self.seed += 1 algo_name = self.local_data[self.ALGO] options = self.local_data.get(self.ALGO_OPTIONS) if options is None: options = {} lib = self._algo_factory.create(algo_name) lib.init_options_grammar(algo_name) if self.SEED in lib.opt_grammar.get_data_names() and self.SEED not in options: options[self.SEED] = self.seed if self.N_SAMPLES in lib.opt_grammar.get_data_names(): n_samples = self.local_data.get(self.N_SAMPLES) if self.N_SAMPLES in options: LOGGER.warning( "Double definition of algorithm option n_samples, keeping value: %s.", n_samples, ) options[self.N_SAMPLES] = n_samples self.optimization_result = lib.execute(self.formulation.opt_problem, **options) return self.optimization_result def _run(self): # type: (...) -> None" ")"*** Start DOE Scenario execution ***")"%s", repr(self)) self._run_algorithm()"*** DOE Scenario run terminated ***") def _update_grammar_input(self): # type: (...) -> None self.input_grammar.update_elements( algo=str, n_samples=int, algo_options=dict, python_typing=True ) self.input_grammar.update_required_elements( algo=True, n_samples=False, algo_options=False )