Source code for gemseo_benchmark.problems.problems_group

# 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: Benoit Pauwels
"""Grouping of reference problems for benchmarking."""
from __future__ import annotations

from pathlib import Path
from typing import Iterable
from typing import Iterator

from gemseo_benchmark.algorithms.algorithms_configurations import (
from gemseo_benchmark.data_profiles.data_profile import DataProfile
from gemseo_benchmark.problems.problem import Problem
from gemseo_benchmark.results.performance_history import PerformanceHistory
from gemseo_benchmark.results.results import Results

[docs]class ProblemsGroup: """A group of reference problems for benchmarking. .. note:: Reference problems should be grouped based on common characteristics such as functions smoothness and constraint set geometry. Attributes: name (str): The name of the group of problems. """ def __init__( self, name: str, problems: Iterable[Problem], description: str = "", ) -> None: """ Args: name: The name of the group of problems. problems: The benchmarking problems of the group. description: The description of the group of problems. """ # noqa: D205, D212, D415 = name self.__problems = problems self.description = description def __iter__(self) -> Iterator[Problem]: return iter(self.__problems)
[docs] def is_algorithm_suited(self, name: str) -> bool: """Check whether an algorithm is suited to all the problems in the group. Args: name: The name of the algorithm. Returns: True if the algorithm is suited. """ return all(problem.is_algorithm_suited(name) for problem in self.__problems)
[docs] def compute_targets( self, targets_number: int, ref_algos_configurations: AlgorithmsConfigurations, only_feasible: bool = True, ) -> None: """Generate targets for all the problems based on given reference algorithms. Args: targets_number: The number of targets to generate. ref_algos_configurations: The configurations of the reference algorithms. only_feasible: Whether to generate only feasible targets. """ for problem in self.__problems: problem.compute_targets( targets_number, ref_algos_configurations, only_feasible )
[docs] def compute_data_profile( self, algos_configurations: AlgorithmsConfigurations, histories_paths: Results, show: bool = True, plot_path: str | Path | None = None, infeasibility_tolerance: float = 0.0, max_eval_number: int | None = None, ) -> None: """Generate the data profiles of given algorithms relative to the problems. Args: algos_configurations: The algorithms configurations. histories_paths: The paths to the reference histories for each algorithm. show: If True, show the plot. plot_path: The path where to save the plot. By default the plot is not saved. infeasibility_tolerance: The tolerance on the infeasibility measure. max_eval_number: The maximum evaluations number to be displayed. If ``None``, this value is inferred from the longest history. """ # Initialize the data profile target_values = { problem.target_values for problem in self.__problems } data_profile = DataProfile(target_values) # Generate the performance histories for configuration_name in algos_configurations.names: for problem in self.__problems: for history_path in histories_paths.get_paths( configuration_name, ): history = PerformanceHistory.from_file(history_path) if max_eval_number is not None: history = history.shorten(max_eval_number) history.apply_infeasibility_tolerance(infeasibility_tolerance) data_profile.add_history(, configuration_name, history.objective_values, history.infeasibility_measures, ) # Plot and/or save the data profile data_profile.plot(show=show, file_path=plot_path)