Source code for pypesto.optimize.optimize

import logging
from typing import Callable, Iterable, Union

from ..engine import Engine, SingleCoreEngine
from ..objective import HistoryOptions
from ..problem import Problem
from ..result import Result
from ..startpoint import StartpointMethod, to_startpoint_method, uniform
from ..store import autosave
from .optimizer import Optimizer, ScipyOptimizer
from .options import OptimizeOptions
from .task import OptimizerTask
from .util import (
    assign_ids,
    bound_n_starts_from_env,
    postprocess_hdf5_history,
    preprocess_hdf5_history,
)

logger = logging.getLogger(__name__)


[docs]def minimize( problem: Problem, optimizer: Optimizer = None, n_starts: int = 100, ids: Iterable[str] = None, startpoint_method: Union[StartpointMethod, Callable, bool] = None, result: Result = None, engine: Engine = None, progress_bar: bool = True, options: OptimizeOptions = None, history_options: HistoryOptions = None, filename: Union[str, Callable, None] = "Auto", overwrite: bool = False, ) -> Result: """ Do multistart optimization. Parameters ---------- problem: The problem to be solved. optimizer: The optimizer to be used n_starts times. n_starts: Number of starts of the optimizer. ids: Ids assigned to the startpoints. startpoint_method: Method for how to choose start points. False means the optimizer does not require start points, e.g. for the 'PyswarmOptimizer'. result: A result object to append the optimization results to. For example, one might append more runs to a previous optimization. If None, a new object is created. engine: Parallelization engine. Defaults to sequential execution on a SingleCoreEngine. progress_bar: Whether to display a progress bar. options: Various options applied to the multistart optimization. history_options: Optimizer history options. filename: Name of the hdf5 file, where the result will be saved. Default is "Auto", in which case it will automatically generate a file named `year_month_day_optimization_result.hdf5`. Deactivate saving by setting filename to `None`. Optionally a method, see docs for `pypesto.store.auto.autosave`. overwrite: Whether to overwrite `result/optimization` in the autosave file if it already exists. Returns ------- result: Result object containing the results of all multistarts in `result.optimize_result`. """ # optimizer if optimizer is None: optimizer = ScipyOptimizer() # number of starts n_starts = bound_n_starts_from_env(n_starts) # startpoint method if startpoint_method is None: startpoint_method = uniform # convert startpoint method to class instance startpoint_method = to_startpoint_method(startpoint_method) # check options if options is None: options = OptimizeOptions() options = OptimizeOptions.assert_instance(options) # history options if history_options is None: history_options = HistoryOptions() history_options = HistoryOptions.assert_instance(history_options) # assign startpoints startpoints = startpoint_method( n_starts=n_starts, problem=problem, ) ids = assign_ids( n_starts=n_starts, ids=ids, result=result, ) # prepare result if result is None: result = Result(problem) # engine if engine is None: engine = SingleCoreEngine() # change to one hdf5 storage file per start if parallel and if hdf5 history_file = history_options.storage_file history_requires_postprocessing = preprocess_hdf5_history( history_options, engine ) # define tasks tasks = [] for startpoint, id in zip(startpoints, ids): task = OptimizerTask( optimizer=optimizer, problem=problem, x0=startpoint, id=id, history_options=history_options, optimize_options=options, ) tasks.append(task) # perform multistart optimization ret = engine.execute(tasks, progress_bar=progress_bar) # merge hdf5 history files if history_requires_postprocessing: postprocess_hdf5_history(ret, history_file, history_options) # aggregate results for optimizer_result in ret: result.optimize_result.append(optimizer_result) # sort by best fval result.optimize_result.sort() # if history file provided, set storage file to that one if filename == "Auto" and history_file is not None: filename = history_file autosave( filename=filename, result=result, store_type="optimize", overwrite=overwrite, ) return result