src/orion/algo/hyperband.py
def suggest(self, num=1):
        """Suggest a number of new sets of parameters.

        Sample new points until first rung is filled. Afterwards
        waits for all trials to be completed before promoting trials
        to the next rung.

        Parameters
        ----------
        num: int, optional
            Number of points to suggest. Defaults to 1.

        Returns
        -------
        list of points or None
            A list of lists representing points suggested by the algorithm. The algorithm may opt
            out if it cannot make a good suggestion at the moment (it may be waiting for other
            trials to complete), in which case it will return None.

        """
        samples = []
        # Temp persist of the points proposed inside this call
        self.points_in_suggest_call = {}
        for bracket in reversed(self.brackets):
            if not bracket.is_filled:
                samples += bracket.sample()

        if samples:
            for sample in samples:
                self.sampled.add(self.get_id(sample, ignore_fidelity=False))
            return samples

        # All brackets are filled

        for bracket in reversed(self.brackets):
            if bracket.is_ready() and not bracket.is_done:
                samples += bracket.promote()

        if samples:
            for sample in samples:
                self.sampled.add(self.get_id(sample, ignore_fidelity=False))
            return samples

        # Either all brackets are done or none are ready and algo needs to wait for some trials to
        # complete
        if len(self.trial_info_wo_fidelity) >= self.space.cardinality:
            logger.warning(
                "The number of unique trials of bottom rungs exceeds the search space "
                "cardinality %i, Hyperband algorithm exits.",
                self.space.cardinality,
            )
        else:
            logger.warning("Hyeprband can not suggest new samples, exit.")

        return None
Suggest a number of new sets of parameters.

        Sample new points until first rung is filled. Afterwards
        waits for all trials to be completed before promoting trials
        to the next rung.

        Parameters
        ----------
        num: int, optional
            Number of points to suggest. Defaults to 1.

        Returns
        -------
        list of points or None
            A list of lists representing points suggested by the algorithm. The algorithm may opt
            out if it cannot make a good suggestion at the moment (it may be waiting for other
            trials to complete), in which case it will return None.

        
Suggest a number of new sets of parameters.
Sample new points until first rung is filled. Afterwards
waits for all trials to be completed before promoting trials
to the next rung.

Parameters

int, optional
Number of points to suggest. Defaults to 1.

Returns

list of points or None
A list of lists representing points suggested by the algorithm. The algorithm may opt
out if it cannot make a good suggestion at the moment (it may be waiting for other
trials to complete), in which case it will return None.
Suggest a number of new sets of parameters . Sample new points until first rung is filled . Afterwards waits for all trials to be completed before promoting trials to the next rung . Parameters int optional Number of points to suggest . Defaults to 1 . Returns list of points or None A list of lists representing points suggested by the algorithm . The algorithm may opt out if it cannot make a good suggestion at the moment ( it may be waiting for other trials to complete ) in which case it will return None .
def suggest(self, num=1):
        samples = []
        self.points_in_suggest_call = {}
        for bracket in reversed(self.brackets):
            if not bracket.is_filled:
                samples += bracket.sample()
        if samples:
            for sample in samples:
                self.sampled.add(self.get_id(sample, ignore_fidelity=False))
            return samples
        for bracket in reversed(self.brackets):
            if bracket.is_ready() and not bracket.is_done:
                samples += bracket.promote()
        if samples:
            for sample in samples:
                self.sampled.add(self.get_id(sample, ignore_fidelity=False))
            return samples
        if len(self.trial_info_wo_fidelity) >= self.space.cardinality:
            logger.warning(
                "The number of unique trials of bottom rungs exceeds the search space "
                "cardinality %i, Hyperband algorithm exits.",
                self.space.cardinality,
            )
        else:
            logger.warning("Hyeprband can not suggest new samples, exit.")
        return None
def suggest ( self , num = 1 ) : samples = [ ] self . points_in_suggest_call = { } for bracket in reversed ( self . brackets ) : if not bracket . is_filled : samples += bracket . sample ( ) if samples : for sample in samples : self . sampled . add ( self . get_id ( sample , ignore_fidelity = False ) ) return samples for bracket in reversed ( self . brackets ) : if bracket . is_ready ( ) and not bracket . is_done : samples += bracket . promote ( ) if samples : for sample in samples : self . sampled . add ( self . get_id ( sample , ignore_fidelity = False ) ) return samples if len ( self . trial_info_wo_fidelity ) >= self . space . cardinality : logger . warning ( "The number of unique trials of bottom rungs exceeds the search space " "cardinality %i, Hyperband algorithm exits." , self . space . cardinality , ) else : logger . warning ( "Hyeprband can not suggest new samples, exit." ) return None
Suggest a number of new sets of parameters.
Suggest a number of new sets of parameters .
"""Suggest a number of new sets of parameters.

        Sample new points until first rung is filled. Afterwards
        waits for all trials to be completed before promoting trials
        to the next rung.

        Parameters
        ----------
        num: int, optional
            Number of points to suggest. Defaults to 1.

        Returns
        -------
        list of points or None
            A list of lists representing points suggested by the algorithm. The algorithm may opt
            out if it cannot make a good suggestion at the moment (it may be waiting for other
            trials to complete), in which case it will return None.

        """ # Temp persist of the points proposed inside this call # All brackets are filled # Either all brackets are done or none are ready and algo needs to wait for some trials to # complete
self
num