Source code for PoliciesMultiPlayers.OracleFair

# -*- coding: utf-8 -*-
""" OracleFair: a multi-player policy which uses a centralized intelligence to affect users an offset, each one take an orthogonal arm based on (offset + t) % nbBestArms, among the best arms.

- It allows to have absolutely *no* collision, if there is more channels than users (always assumed).
- And it is perfectly fair on every run: each chosen arm is played successively by each player.
- Note that it IS affecting players on the best arms: it requires full knowledge of the means of the arms, not simply the number of arms.

- Note that they need a perfect knowledge on the arms, even this is not physically plausible.
from __future__ import division, print_function  # Python 2 compatibility

__author__ = "Lilian Besson"
__version__ = "0.1"

import numpy as np

    from .BaseMPPolicy import BaseMPPolicy
    from .BaseCentralizedPolicy import BaseCentralizedPolicy
    from .ChildPointer import ChildPointer
except ImportError:
    from BaseMPPolicy import BaseMPPolicy
    from BaseCentralizedPolicy import BaseCentralizedPolicy
    from ChildPointer import ChildPointer

[docs]class CyclingBest(BaseCentralizedPolicy): """ CyclingBest: select an arm in the best ones (bestArms) as (offset + t) % (len(bestArms)), with offset being decided by the OracleFair multi-player policy. """
[docs] def __init__(self, nbArms, offset, bestArms=None): """Cycling with an offset.""" self.nbArms = nbArms #: Number of arms self.offset = offset #: Offset if bestArms is None: bestArms = list(range(nbArms)) self.bestArms = bestArms #: List of index of the best arms to play self.nb_bestArms = len(bestArms) #: Number of best arms self.t = -1 #: Internal time
[docs] def __str__(self): return "CyclingBest({}, {})".format(self.offset, self.bestArms)
[docs] def startGame(self): """Nothing to do.""" pass
[docs] def getReward(self, arm, reward): """Nothing to do.""" pass
[docs] def choice(self): """Chose cycling arm.""" self.t += 1 return self.bestArms[(self.offset + self.t) % self.nb_bestArms]
[docs]class OracleFair(BaseMPPolicy): """ OracleFair: a multi-player policy which uses a centralize intelligence to affect users an offset, each one take an orthogonal arm based on (offset + t) % nbArms. """
[docs] def __init__(self, nbPlayers, armsMAB, lower=0., amplitude=1.): """ - nbPlayers: number of players to create (in self._players). - armsMAB: MAB object that represents the arms. Examples: >>> import sys; sys.path.insert(0, '..'); from Environment import MAB; from Arms import Bernoulli >>> import random; random.seed(0); import numpy as np; np.random.seed(0) >>> problem = MAB({'arm_type': Bernoulli, 'params': [0.1, 0.5, 0.9]}) # doctest: +ELLIPSIS,+NORMALIZE_WHITESPACE ... >>> s = OracleFair(2, problem) >>> [ child.choice() for child in s.children ] [1, 2] >>> [ child.choice() for child in s.children ] [2, 1] - To get a list of usable players, use ``s.children``. - Warning: ``s._players`` is for internal use """ assert nbPlayers > 0, "Error, the parameter 'nbPlayers' for OracleFair class has to be > 0." nbArms = armsMAB.nbArms if nbPlayers > nbArms: print("Warning, there is more users than arms ... (nbPlayers > nbArms)") # XXX # Attributes self.nbPlayers = nbPlayers #: Number of players self.nbArms = nbArms #: Number of arms # Internal vectorial memory means = np.array([arm.mean for arm in armsMAB.arms]) bestArms = np.argsort(means)[-min(nbPlayers, nbArms):] # print("bestArms =", bestArms) # DEBUG if nbPlayers <= nbArms: self._offsets = np.argsort(means)[-nbPlayers:] # Decide the offsets of the centralized players else: self._offsets = np.zeros(nbPlayers, dtype=int) self._offsets[:nbArms] = np.random.permutation(nbArms) # Try to minimize the number of doubled offsets, so all the other players are affected to the *same* arm worseArm = np.argmin(means) self._offsets[nbArms:] = worseArm # FIXME improve this, when there is more player than arms, this is not optimal # indeed, all the collisions will first be in worseArm, but they all cycle! That's bad # XXX this "trash" arm with max number of collision will cycle: that's the best we can do! # self._offsets[nbArms:] = np.random.choice(nbArms, size=nbPlayers - nbArms, replace=True) # Shuffle it once, just to be fair in average np.random.shuffle(self._offsets) # print("OracleFair: initialized with {} arms and {} players ...".format(nbArms, nbPlayers)) # DEBUG # print("It decided to use this affectation of arms :") # DEBUG # Internal object memory self._players = [None] * nbPlayers self.children = [None] * nbPlayers #: List of children, fake algorithms for playerId in range(nbPlayers): # print(" - Player number {} will use an offset of {} ...".format(playerId + 1, self._offsets[playerId])) # DEBUG self._players[playerId] = CyclingBest(nbArms, self._offsets[playerId], bestArms) self.children[playerId] = ChildPointer(self, playerId) self._printNbCollisions() # DEBUG
[docs] def __str__(self): return "OracleFair({} x {})".format(self.nbPlayers, str(self._players[0]))
[docs] def _printNbCollisions(self): """ Print number of collisions. """ nbPlayersAlone = len(set(self._offsets)) if nbPlayersAlone != self.nbPlayers: print("\n==> This affectation will bring collisions! Exactly {} at each step...".format(self.nbPlayers - nbPlayersAlone)) for armId in range(self.nbArms): nbAffected = np.count_nonzero(self._offsets == armId) if nbAffected > 1: print(" - For arm number {}, there is {} different child player affected on this arm ...".format(armId + 1, nbAffected))