Пример #1
0
class AbstractTAFunc(SerialRunner):
    """Baseclass to execute target algorithms which are python functions.

    **Note:*** Do not use directly

    Parameters
    ----------
    ta : callable
        Function (target algorithm) to be optimized.
    stats: Stats()
         stats object to collect statistics about runtime and so on
    multi_objectives: List[str]
        names of the objectives, by default it is a single objective parameter "cost"
    run_obj: str
        run objective of SMAC
    memory_limit : int, optional
        Memory limit (in MB) that will be applied to the target algorithm.
    par_factor: int
        penalization factor
    cost_for_crash : float
        cost that is used in case of crashed runs (including runs
        that returned NaN or inf)
    use_pynisher: bool
        use pynisher to limit resources;
        if disabled
          * TA func can use as many resources
          as it wants (time and memory) --- use with caution
          * all runs will be returned as SUCCESS if returned value is not None

    Attributes
    ----------
    memory_limit
    use_pynisher
    """

    def __init__(
        self,
        ta: Callable,
        stats: Stats,
        multi_objectives: List[str] = ["cost"],
        run_obj: str = "quality",
        memory_limit: Optional[int] = None,
        par_factor: int = 1,
        cost_for_crash: float = float(MAXINT),
        abort_on_first_run_crash: bool = False,
        use_pynisher: bool = True,
    ):

        super().__init__(
            ta=ta,
            stats=stats,
            multi_objectives=multi_objectives,
            run_obj=run_obj,
            par_factor=par_factor,
            cost_for_crash=cost_for_crash,
            abort_on_first_run_crash=abort_on_first_run_crash,
        )
        self.ta = ta
        self.stats = stats
        self.multi_objectives = multi_objectives
        self.run_obj = run_obj

        self.par_factor = par_factor
        self.cost_for_crash = cost_for_crash
        self.abort_on_first_run_crash = abort_on_first_run_crash

        signature = inspect.signature(ta).parameters
        self._accepts_seed = "seed" in signature.keys()
        self._accepts_instance = "instance" in signature.keys()
        self._accepts_budget = "budget" in signature.keys()
        if not callable(ta):
            raise TypeError("Argument `ta` must be a callable, but is %s" % type(ta))
        self._ta = cast(Callable, ta)

        if memory_limit is not None:
            memory_limit = int(math.ceil(memory_limit))
        self.memory_limit = memory_limit

        self.use_pynisher = use_pynisher

        self.logger = PickableLoggerAdapter(
            self.__module__ + "." + self.__class__.__name__
        )

    def run(
        self,
        config: Configuration,
        instance: Optional[str] = None,
        cutoff: Optional[float] = None,
        seed: int = 12345,
        budget: Optional[float] = None,
        instance_specific: str = "0",
    ) -> Tuple[StatusType, float, float, Dict]:
        """Runs target algorithm <self._ta> with configuration <config> for at
        most <cutoff> seconds, allowing it to use at most <memory_limit> RAM.

        Whether the target algorithm is called with the <instance> and
        <seed> depends on the subclass implementing the actual call to
        the target algorithm.

        Parameters
        ----------
            config : Configuration, dictionary (or similar)
                Dictionary param -> value
            instance : str, optional
                Problem instance
            cutoff : float, optional
                Wallclock time limit of the target algorithm. If no value is
                provided no limit will be enforced. It is casted to integer internally.
            seed : int
                Random seed
            budget : float, optional
                A positive, real-valued number representing an arbitrary limit to the target algorithm
                Handled by the target algorithm internally
            instance_specific: str
                Instance specific information (e.g., domain file or solution)
        Returns
        -------
            status: enum of StatusType (int)
                {SUCCESS, TIMEOUT, CRASHED, ABORT}
            cost: np.ndarray
                cost/regret/quality/runtime (float) (None, if not returned by TA)
            runtime: float
                runtime (None if not returned by TA)
            additional_info: dict
                all further additional run information
        """

        obj_kwargs = {}  # type: Dict[str, Union[int, str, float, None]]
        if self._accepts_seed:
            obj_kwargs["seed"] = seed
        if self._accepts_instance:
            obj_kwargs["instance"] = instance
        if self._accepts_budget:
            obj_kwargs["budget"] = budget

        cost = self.cost_for_crash  # type: Union[float, List[float]]

        if self.use_pynisher:
            # walltime for pynisher has to be a rounded up integer
            if cutoff is not None:
                cutoff = int(math.ceil(cutoff))
                if cutoff > MAX_CUTOFF:
                    raise ValueError(
                        "%d is outside the legal range of [0, 65535] "
                        "for cutoff (when using pynisher, due to OS limitations)"
                        % cutoff
                    )

            arguments = {
                "logger": self.logger,
                "wall_time_in_s": cutoff,
                "mem_in_mb": self.memory_limit,
            }

            # call ta
            try:
                obj = pynisher.enforce_limits(**arguments)(self._ta)
                rval = self._call_ta(obj, config, obj_kwargs)
            except Exception as e:
                cost = np.asarray(cost).squeeze().tolist()
                exception_traceback = traceback.format_exc()
                error_message = repr(e)
                additional_info = {
                    "traceback": exception_traceback,
                    "error": error_message,
                }

                return StatusType.CRASHED, cost, 0.0, additional_info  # type: ignore

            if isinstance(rval, tuple):
                result = rval[0]
                additional_run_info = rval[1]
            else:
                result = rval
                additional_run_info = {}

            # get status, cost, time
            if obj.exit_status is pynisher.TimeoutException:
                status = StatusType.TIMEOUT
            elif obj.exit_status is pynisher.MemorylimitException:
                status = StatusType.MEMOUT
            elif obj.exit_status == 0 and result is not None:
                status = StatusType.SUCCESS
                cost = result  # type: ignore # noqa
            else:
                status = StatusType.CRASHED

            runtime = float(obj.wall_clock_time)
        else:
            start_time = time.time()

            # call ta
            try:
                rval = self._call_ta(self._ta, config, obj_kwargs)

                if isinstance(rval, tuple):
                    result = rval[0]
                    additional_run_info = rval[1]
                else:
                    result = rval
                    additional_run_info = {}

                status = StatusType.SUCCESS
                cost = result  # type: ignore
            except Exception as e:
                self.logger.exception(e)
                status = StatusType.CRASHED
                additional_run_info = {}

            runtime = time.time() - start_time

        # Do some sanity checking (for multi objective)
        if len(self.multi_objectives) > 1:
            error = f"Returned costs {cost} does not match the number of objectives {len(self.multi_objectives)}."

            # If dict convert to array
            # Make sure the ordering is correct
            if isinstance(cost, dict):
                ordered_cost = []
                for name in self.multi_objectives:
                    if name not in cost:
                        raise RuntimeError(
                            f"Objective {name} was not found in the returned costs."
                        )

                    ordered_cost.append(cost[name])
                cost = ordered_cost

            if isinstance(cost, list):
                if len(cost) != len(self.multi_objectives):
                    raise RuntimeError(error)

            if isinstance(cost, float):
                raise RuntimeError(error)

        if cost is None or status == StatusType.CRASHED:
            status = StatusType.CRASHED
            cost = self.cost_for_crash

        cost = np.asarray(cost).squeeze().tolist()

        return status, cost, runtime, additional_run_info  # type: ignore

    def _call_ta(
        self,
        obj: Callable,
        config: Configuration,
        obj_kwargs: Dict[str, Union[int, str, float, None]],
    ) -> Union[float, Tuple[float, Dict]]:
        raise NotImplementedError()
Пример #2
0
class AbstractTAFunc(SerialRunner):
    """Baseclass to execute target algorithms which are python functions.

    **Note:*** Do not use directly

    Attributes
    ----------
    memory_limit
    use_pynisher
    """
    def __init__(
        self,
        ta: typing.Callable,
        stats: Stats,
        run_obj: str = "quality",
        memory_limit: typing.Optional[int] = None,
        par_factor: int = 1,
        cost_for_crash: float = float(MAXINT),
        abort_on_first_run_crash: bool = False,
        use_pynisher: bool = True,
    ):

        super().__init__(
            ta=ta,
            stats=stats,
            run_obj=run_obj,
            par_factor=par_factor,
            cost_for_crash=cost_for_crash,
            abort_on_first_run_crash=abort_on_first_run_crash,
        )
        """
        Abstract class for having a function as target algorithm

        Parameters
        ----------
        ta : callable
            Function (target algorithm) to be optimized.
        stats: Stats()
             stats object to collect statistics about runtime and so on
        run_obj: str
            run objective of SMAC
        memory_limit : int, optional
            Memory limit (in MB) that will be applied to the target algorithm.
        par_factor: int
            penalization factor
        cost_for_crash : float
            cost that is used in case of crashed runs (including runs
            that returned NaN or inf)
        use_pynisher: bool
            use pynisher to limit resources;
            if disabled
              * TA func can use as many resources
              as it wants (time and memory) --- use with caution
              * all runs will be returned as SUCCESS if returned value is not None
        """
        self.ta = ta
        self.stats = stats
        self.run_obj = run_obj

        self.par_factor = par_factor
        self.cost_for_crash = cost_for_crash
        self.abort_on_first_run_crash = abort_on_first_run_crash

        signature = inspect.signature(ta).parameters
        self._accepts_seed = 'seed' in signature.keys()
        self._accepts_instance = 'instance' in signature.keys()
        self._accepts_budget = 'budget' in signature.keys()
        if not callable(ta):
            raise TypeError('Argument `ta` must be a callable, but is %s' %
                            type(ta))
        self._ta = typing.cast(typing.Callable, ta)

        if memory_limit is not None:
            memory_limit = int(math.ceil(memory_limit))
        self.memory_limit = memory_limit

        self.use_pynisher = use_pynisher

        self.logger = PickableLoggerAdapter(self.__module__ + '.' +
                                            self.__class__.__name__)

    def run(
        self,
        config: Configuration,
        instance: typing.Optional[str] = None,
        cutoff: typing.Optional[float] = None,
        seed: int = 12345,
        budget: typing.Optional[float] = None,
        instance_specific: str = "0"
    ) -> typing.Tuple[StatusType, float, float, typing.Dict]:
        """Runs target algorithm <self._ta> with configuration <config> for at
        most <cutoff> seconds, allowing it to use at most <memory_limit> RAM.

        Whether the target algorithm is called with the <instance> and
        <seed> depends on the subclass implementing the actual call to
        the target algorithm.

        Parameters
        ----------
            config : Configuration, dictionary (or similar)
                Dictionary param -> value
            instance : str, optional
                Problem instance
            cutoff : float, optional
                Wallclock time limit of the target algorithm. If no value is
                provided no limit will be enforced. It is casted to integer internally.
            seed : int
                Random seed
            budget : float, optional
                A positive, real-valued number representing an arbitrary limit to the target algorithm
                Handled by the target algorithm internally
            instance_specific: str
                Instance specific information (e.g., domain file or solution)
        Returns
        -------
            status: enum of StatusType (int)
                {SUCCESS, TIMEOUT, CRASHED, ABORT}
            cost: float
                cost/regret/quality/runtime (float) (None, if not returned by TA)
            runtime: float
                runtime (None if not returned by TA)
            additional_info: dict
                all further additional run information
        """

        obj_kwargs = {
        }  # type: typing.Dict[str, typing.Union[int, str, float, None]]
        if self._accepts_seed:
            obj_kwargs['seed'] = seed
        if self._accepts_instance:
            obj_kwargs['instance'] = instance
        if self._accepts_budget:
            obj_kwargs['budget'] = budget

        if self.use_pynisher:
            # walltime for pynisher has to be a rounded up integer
            if cutoff is not None:
                cutoff = int(math.ceil(cutoff))
                if cutoff > MAX_CUTOFF:
                    raise ValueError(
                        "%d is outside the legal range of [0, 65535] "
                        "for cutoff (when using pynisher, due to OS limitations)"
                        % cutoff)

            arguments = {
                'logger': self.logger,
                'wall_time_in_s': cutoff,
                'mem_in_mb': self.memory_limit
            }

            # call ta
            try:
                obj = pynisher.enforce_limits(**arguments)(self._ta)
                rval = self._call_ta(obj, config, obj_kwargs)
            except Exception as e:
                exception_traceback = traceback.format_exc()
                error_message = repr(e)
                additional_info = {
                    'traceback': exception_traceback,
                    'error': error_message
                }
                return StatusType.CRASHED, self.cost_for_crash, 0.0, additional_info

            if isinstance(rval, tuple):
                result = rval[0]
                additional_run_info = rval[1]
            else:
                result = rval
                additional_run_info = {}

            # get status, cost, time
            if obj.exit_status is pynisher.TimeoutException:
                status = StatusType.TIMEOUT
                cost = self.cost_for_crash
            elif obj.exit_status is pynisher.MemorylimitException:
                status = StatusType.MEMOUT
                cost = self.cost_for_crash
            elif obj.exit_status == 0 and result is not None:
                status = StatusType.SUCCESS
                cost = result
            else:
                status = StatusType.CRASHED
                cost = self.cost_for_crash

            runtime = float(obj.wall_clock_time)
        else:
            start_time = time.time()
            # call ta
            try:
                rval = self._call_ta(self._ta, config, obj_kwargs)
                if isinstance(rval, tuple):
                    result = rval[0]
                    additional_run_info = rval[1]
                else:
                    result = rval
                    additional_run_info = {}
                status = StatusType.SUCCESS
                cost = result
            except Exception as e:
                self.logger.exception(e)
                cost, result = self.cost_for_crash, self.cost_for_crash
                status = StatusType.CRASHED
                additional_run_info = {}

            runtime = time.time() - start_time

        if status == StatusType.SUCCESS and not isinstance(
                result, (int, float)):
            status = StatusType.CRASHED
            cost = self.cost_for_crash

        return status, cost, runtime, additional_run_info

    def _call_ta(
        self,
        obj: typing.Callable,
        config: Configuration,
        obj_kwargs: typing.Dict[str, typing.Union[int, str, float, None]],
    ) -> typing.Union[float, typing.Tuple[float, typing.Dict]]:
        raise NotImplementedError()