Source code for openmdao.drivers.doe_driver

Design-of-Experiments Driver.

import traceback
import inspect

import numpy as np

from openmdao.core.driver import Driver, RecordingDebugging
from openmdao.core.analysis_error import AnalysisError
from openmdao.drivers.doe_generators import DOEGenerator, ListGenerator

from openmdao.utils.mpi import MPI

from openmdao.recorders.sqlite_recorder import SqliteRecorder

[docs]class DOEDriver(Driver): """ Design-of-Experiments Driver. Attributes ---------- _name : str The name used to identify this driver in recorded cases. _problem_comm : MPI.Comm or None The MPI communicator for the Problem. _color : int or None In MPI, the cached color is used to determine which cases to run on this proc. """
[docs] def __init__(self, generator=None, **kwargs): """ Construct A DOEDriver. Parameters ---------- generator : DOEGenerator, list or None The case generator or a list of DOE cases. **kwargs : dict of keyword arguments Keyword arguments that will be mapped into the Driver options. """ # if given a list, create a ListGenerator if isinstance(generator, list): generator = ListGenerator(generator) elif generator and not isinstance(generator, DOEGenerator): if inspect.isclass(generator): raise TypeError("DOEDriver requires an instance of DOEGenerator, " "but a class object was found: %s" % generator.__name__) else: raise TypeError("DOEDriver requires an instance of DOEGenerator, " "but an instance of %s was found." % type(generator).__name__) super().__init__(**kwargs) # What we support self.supports['integer_design_vars'] = True # What we don't support self.supports['distributed_design_vars'] = False self.supports._read_only = True if generator is not None: self.options['generator'] = generator self._name = '' self._problem_comm = None self._color = None
def _declare_options(self): """ Declare options before kwargs are processed in the init method. """ self.options.declare('generator', types=(DOEGenerator), default=DOEGenerator(), desc='The case generator. If default, no cases are generated.') self.options.declare('run_parallel', types=bool, default=False, desc='Set to True to execute cases in parallel.') self.options.declare('procs_per_model', types=int, default=1, lower=1, desc='Number of processors to give each model under MPI.') def _setup_comm(self, comm): """ Perform any driver-specific setup of communicators for the model. Parameters ---------- comm : MPI.Comm or <FakeComm> or None The communicator for the Problem. Returns ------- MPI.Comm or <FakeComm> or None The communicator for the Problem model. """ self._problem_comm = comm if MPI: procs_per_model = self.options['procs_per_model'] full_size = comm.size size = full_size // procs_per_model if full_size != size * procs_per_model: raise RuntimeError("The total number of processors is not evenly divisible by the " "specified number of processors per model.\n Provide a " "number of processors that is a multiple of %d, or " "specify a number of processors per model that divides " "into %d." % (procs_per_model, full_size)) color = self._color = comm.rank % size model_comm = comm.Split(color) else: model_comm = comm return model_comm def _set_name(self): """ Set the name of this DOE driver and its case generator. Returns ------- str The name of this DOE driver and its case generator. """ generator = self.options['generator'] gen_type = type(generator).__name__.replace('Generator', '') if gen_type == 'DOEGenerator': self._name = 'DOEDriver' # Empty generator else: self._name = 'DOEDriver_' + gen_type return self._name def _get_name(self): """ Get the name of this DOE driver and its case generator. Returns ------- str The name of this DOE driver and its case generator. """ return self._name
[docs] def run(self): """ Generate cases and run the model for each set of generated input values. Returns ------- boolean Failure flag; True if failed to converge, False is successful. """ self.iter_count = 0 # set driver name with current generator self._set_name() if MPI and self.options['run_parallel']: case_gen = self._parallel_generator else: case_gen = self.options['generator'] for case in case_gen(self._designvars, self._problem().model): self._run_case(case) self.iter_count += 1 return False
def _run_case(self, case): """ Run case, save exception info and mark the metadata if the case fails. Parameters ---------- case : list list of name, value tuples for the design variables. """ metadata = {} for dv_name, dv_val in case: try: msg = None if isinstance(dv_val, np.ndarray): self.set_design_var(dv_name, dv_val.flatten()) else: self.set_design_var(dv_name, dv_val) except ValueError as err: msg = "Error assigning %s = %s: " % (dv_name, dv_val) + str(err) finally: if msg: raise(ValueError(msg)) with RecordingDebugging(self._get_name(), self.iter_count, self) as rec: try: self._problem().model.run_solve_nonlinear() metadata['success'] = 1 metadata['msg'] = '' except AnalysisError: metadata['success'] = 0 metadata['msg'] = traceback.format_exc() except Exception: metadata['success'] = 0 metadata['msg'] = traceback.format_exc() print(metadata['msg']) # save reference to metadata for use in record_iteration self._metadata = metadata def _parallel_generator(self, design_vars, model=None): """ Generate case for this processor when running under MPI. Parameters ---------- design_vars : dict Dictionary of design variables for which to generate values. model : Group The model containing the design variables (used by some generators). Yields ------ list list of name, value tuples for the design variables. """ size = self._problem_comm.size // self.options['procs_per_model'] color = self._color generator = self.options['generator'] for i, case in enumerate(generator(design_vars, model)): if i % size == color: yield case def _setup_recording(self): """ Set up case recording. """ if MPI: procs_per_model = self.options['procs_per_model'] for recorder in self._rec_mgr: recorder._parallel = True # if SqliteRecorder, write cases only on procs up to the number # of parallel DOEs (i.e. on the root procs for the cases) if isinstance(recorder, SqliteRecorder): if procs_per_model == 1: recorder._record_on_proc = True else: size = self._problem_comm.size // procs_per_model if self._problem_comm.rank < size: recorder._record_on_proc = True else: recorder._record_on_proc = False super()._setup_recording() def _get_recorder_metadata(self, case_name): """ Return metadata from the latest iteration for use in the recorder. Parameters ---------- case_name : str Name of current case. Returns ------- dict Metadata dictionary for the recorder. """ self._metadata['name'] = case_name return self._metadata