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727 lines
27 KiB
Python
727 lines
27 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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trueskill
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~~~~~~~~~
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The video game rating system.
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:copyright: (c) 2012-2016 by Heungsub Lee
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:license: BSD, see LICENSE for more details.
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"""
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from __future__ import absolute_import
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from itertools import chain
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import math
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from six import iteritems
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from six.moves import map, range, zip
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from .__about__ import __version__ # noqa
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from .backends import choose_backend
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from .factorgraph import (LikelihoodFactor, PriorFactor, SumFactor,
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TruncateFactor, Variable)
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from .mathematics import Gaussian, Matrix
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__all__ = [
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# TrueSkill objects
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'TrueSkill', 'Rating',
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# functions for the global environment
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'rate', 'quality', 'rate_1vs1', 'quality_1vs1', 'expose', 'setup',
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'global_env',
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# default values
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'MU', 'SIGMA', 'BETA', 'TAU', 'DRAW_PROBABILITY',
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# draw probability helpers
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'calc_draw_probability', 'calc_draw_margin',
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# deprecated features
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'transform_ratings', 'match_quality', 'dynamic_draw_probability',
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]
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#: Default initial mean of ratings.
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MU = 25.
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#: Default initial standard deviation of ratings.
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SIGMA = MU / 3
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#: Default distance that guarantees about 76% chance of winning.
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BETA = SIGMA / 2
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#: Default dynamic factor.
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TAU = SIGMA / 100
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#: Default draw probability of the game.
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DRAW_PROBABILITY = .10
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#: A basis to check reliability of the result.
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DELTA = 0.0001
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def calc_draw_probability(draw_margin, size, env=None):
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"""Calculates a draw-probability from the given ``draw_margin``.
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:param draw_margin: the draw-margin.
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:param size: the number of players in two comparing teams.
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:param env: the :class:`TrueSkill` object. Defaults to the global
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environment.
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"""
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if env is None:
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env = global_env()
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return 2 * env.cdf(draw_margin / (math.sqrt(size) * env.beta)) - 1
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def calc_draw_margin(draw_probability, size, env=None):
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"""Calculates a draw-margin from the given ``draw_probability``.
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:param draw_probability: the draw-probability.
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:param size: the number of players in two comparing teams.
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:param env: the :class:`TrueSkill` object. Defaults to the global
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environment.
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"""
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if env is None:
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env = global_env()
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return env.ppf((draw_probability + 1) / 2.) * math.sqrt(size) * env.beta
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def _team_sizes(rating_groups):
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"""Makes a size map of each teams."""
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team_sizes = [0]
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for group in rating_groups:
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team_sizes.append(len(group) + team_sizes[-1])
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del team_sizes[0]
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return team_sizes
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def _floating_point_error(env):
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if env.backend == 'mpmath':
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msg = 'Set "mpmath.mp.dps" to higher'
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else:
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msg = 'Cannot calculate correctly, set backend to "mpmath"'
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return FloatingPointError(msg)
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class Rating(Gaussian):
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"""Represents a player's skill as Gaussian distrubution.
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The default mu and sigma value follows the global environment's settings.
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If you don't want to use the global, use :meth:`TrueSkill.create_rating` to
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create the rating object.
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:param mu: the mean.
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:param sigma: the standard deviation.
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"""
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def __init__(self, mu=None, sigma=None):
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if isinstance(mu, tuple):
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mu, sigma = mu
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elif isinstance(mu, Gaussian):
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mu, sigma = mu.mu, mu.sigma
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if mu is None:
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mu = global_env().mu
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if sigma is None:
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sigma = global_env().sigma
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super(Rating, self).__init__(mu, sigma)
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def __int__(self):
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return int(self.mu)
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def __long__(self):
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return long(self.mu)
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def __float__(self):
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return float(self.mu)
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def __iter__(self):
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return iter((self.mu, self.sigma))
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def __repr__(self):
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c = type(self)
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args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma)
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return '%s(mu=%.3f, sigma=%.3f)' % args
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class TrueSkill(object):
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"""Implements a TrueSkill environment. An environment could have
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customized constants. Every games have not same design and may need to
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customize TrueSkill constants.
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For example, 60% of matches in your game have finished as draw then you
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should set ``draw_probability`` to 0.60::
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env = TrueSkill(draw_probability=0.60)
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For more details of the constants, see `The Math Behind TrueSkill`_ by
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Jeff Moser.
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.. _The Math Behind TrueSkill:: http://bit.ly/trueskill-math
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:param mu: the initial mean of ratings.
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:param sigma: the initial standard deviation of ratings. The recommended
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value is a third of ``mu``.
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:param beta: the distance which guarantees about 76% chance of winning.
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The recommended value is a half of ``sigma``.
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:param tau: the dynamic factor which restrains a fixation of rating. The
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recommended value is ``sigma`` per cent.
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:param draw_probability: the draw probability between two teams. It can be
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a ``float`` or function which returns a ``float``
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by the given two rating (team performance)
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arguments and the beta value. If it is a
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``float``, the game has fixed draw probability.
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Otherwise, the draw probability will be decided
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dynamically per each match.
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:param backend: the name of a backend which implements cdf, pdf, ppf. See
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:mod:`trueskill.backends` for more details. Defaults to
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``None``.
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"""
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def __init__(self, mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
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draw_probability=DRAW_PROBABILITY, backend=None):
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self.mu = mu
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self.sigma = sigma
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self.beta = beta
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self.tau = tau
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self.draw_probability = draw_probability
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self.backend = backend
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if isinstance(backend, tuple):
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self.cdf, self.pdf, self.ppf = backend
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else:
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self.cdf, self.pdf, self.ppf = choose_backend(backend)
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def create_rating(self, mu=None, sigma=None):
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"""Initializes new :class:`Rating` object, but it fixes default mu and
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sigma to the environment's.
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>>> env = TrueSkill(mu=0, sigma=1)
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>>> env.create_rating()
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trueskill.Rating(mu=0.000, sigma=1.000)
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"""
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if mu is None:
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mu = self.mu
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if sigma is None:
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sigma = self.sigma
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return Rating(mu, sigma)
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def v_win(self, diff, draw_margin):
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"""The non-draw version of "V" function. "V" calculates a variation of
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a mean.
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"""
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x = diff - draw_margin
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denom = self.cdf(x)
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return (self.pdf(x) / denom) if denom else -x
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def v_draw(self, diff, draw_margin):
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"""The draw version of "V" function."""
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abs_diff = abs(diff)
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a, b = draw_margin - abs_diff, -draw_margin - abs_diff
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denom = self.cdf(a) - self.cdf(b)
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numer = self.pdf(b) - self.pdf(a)
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return ((numer / denom) if denom else a) * (-1 if diff < 0 else +1)
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def w_win(self, diff, draw_margin):
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"""The non-draw version of "W" function. "W" calculates a variation of
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a standard deviation.
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"""
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x = diff - draw_margin
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v = self.v_win(diff, draw_margin)
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w = v * (v + x)
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if 0 < w < 1:
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return w
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raise _floating_point_error(self)
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def w_draw(self, diff, draw_margin):
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"""The draw version of "W" function."""
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abs_diff = abs(diff)
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a, b = draw_margin - abs_diff, -draw_margin - abs_diff
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denom = self.cdf(a) - self.cdf(b)
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if not denom:
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raise _floating_point_error(self)
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v = self.v_draw(abs_diff, draw_margin)
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return (v ** 2) + (a * self.pdf(a) - b * self.pdf(b)) / denom
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def validate_rating_groups(self, rating_groups):
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"""Validates a ``rating_groups`` argument. It should contain more than
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2 groups and all groups must not be empty.
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>>> env = TrueSkill()
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>>> env.validate_rating_groups([])
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Traceback (most recent call last):
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...
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ValueError: need multiple rating groups
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>>> env.validate_rating_groups([(Rating(),)])
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Traceback (most recent call last):
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...
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ValueError: need multiple rating groups
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>>> env.validate_rating_groups([(Rating(),), ()])
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Traceback (most recent call last):
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...
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ValueError: each group must contain multiple ratings
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>>> env.validate_rating_groups([(Rating(),), (Rating(),)])
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... #doctest: +ELLIPSIS
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[(truekill.Rating(...),), (trueskill.Rating(...),)]
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"""
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# check group sizes
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if len(rating_groups) < 2:
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raise ValueError('Need multiple rating groups')
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elif not all(rating_groups):
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raise ValueError('Each group must contain multiple ratings')
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# check group types
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group_types = set(map(type, rating_groups))
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if len(group_types) != 1:
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raise TypeError('All groups should be same type')
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elif group_types.pop() is Rating:
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raise TypeError('Rating cannot be a rating group')
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# normalize rating_groups
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if isinstance(rating_groups[0], dict):
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dict_rating_groups = rating_groups
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rating_groups = []
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keys = []
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for dict_rating_group in dict_rating_groups:
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rating_group, key_group = [], []
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for key, rating in iteritems(dict_rating_group):
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rating_group.append(rating)
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key_group.append(key)
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rating_groups.append(tuple(rating_group))
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keys.append(tuple(key_group))
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else:
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rating_groups = list(rating_groups)
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keys = None
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return rating_groups, keys
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def validate_weights(self, weights, rating_groups, keys=None):
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if weights is None:
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weights = [(1,) * len(g) for g in rating_groups]
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elif isinstance(weights, dict):
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weights_dict, weights = weights, []
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for x, group in enumerate(rating_groups):
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w = []
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weights.append(w)
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for y, rating in enumerate(group):
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if keys is not None:
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y = keys[x][y]
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w.append(weights_dict.get((x, y), 1))
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return weights
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def factor_graph_builders(self, rating_groups, ranks, weights):
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"""Makes nodes for the TrueSkill factor graph.
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Here's an example of a TrueSkill factor graph when 1 vs 2 vs 1 match::
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rating_layer: O O O O (PriorFactor)
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perf_layer: O O O O (LikelihoodFactor)
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| \ / |
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team_perf_layer: O O O (SumFactor)
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\ / \ /
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team_diff_layer: O O (SumFactor)
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trunc_layer: O O (TruncateFactor)
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"""
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flatten_ratings = sum(map(tuple, rating_groups), ())
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flatten_weights = sum(map(tuple, weights), ())
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size = len(flatten_ratings)
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group_size = len(rating_groups)
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# create variables
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rating_vars = [Variable() for x in range(size)]
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perf_vars = [Variable() for x in range(size)]
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team_perf_vars = [Variable() for x in range(group_size)]
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team_diff_vars = [Variable() for x in range(group_size - 1)]
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team_sizes = _team_sizes(rating_groups)
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# layer builders
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def build_rating_layer():
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for rating_var, rating in zip(rating_vars, flatten_ratings):
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yield PriorFactor(rating_var, rating, self.tau)
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def build_perf_layer():
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for rating_var, perf_var in zip(rating_vars, perf_vars):
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yield LikelihoodFactor(rating_var, perf_var, self.beta ** 2)
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def build_team_perf_layer():
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for team, team_perf_var in enumerate(team_perf_vars):
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if team > 0:
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start = team_sizes[team - 1]
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else:
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start = 0
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end = team_sizes[team]
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child_perf_vars = perf_vars[start:end]
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coeffs = flatten_weights[start:end]
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yield SumFactor(team_perf_var, child_perf_vars, coeffs)
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def build_team_diff_layer():
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for team, team_diff_var in enumerate(team_diff_vars):
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yield SumFactor(team_diff_var,
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team_perf_vars[team:team + 2], [+1, -1])
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def build_trunc_layer():
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for x, team_diff_var in enumerate(team_diff_vars):
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if callable(self.draw_probability):
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# dynamic draw probability
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team_perf1, team_perf2 = team_perf_vars[x:x + 2]
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args = (Rating(team_perf1), Rating(team_perf2), self)
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draw_probability = self.draw_probability(*args)
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else:
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# static draw probability
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draw_probability = self.draw_probability
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size = sum(map(len, rating_groups[x:x + 2]))
|
||
|
|
draw_margin = calc_draw_margin(draw_probability, size, self)
|
||
|
|
if ranks[x] == ranks[x + 1]: # is a tie?
|
||
|
|
v_func, w_func = self.v_draw, self.w_draw
|
||
|
|
else:
|
||
|
|
v_func, w_func = self.v_win, self.w_win
|
||
|
|
yield TruncateFactor(team_diff_var,
|
||
|
|
v_func, w_func, draw_margin)
|
||
|
|
# build layers
|
||
|
|
return (build_rating_layer, build_perf_layer, build_team_perf_layer,
|
||
|
|
build_team_diff_layer, build_trunc_layer)
|
||
|
|
|
||
|
|
def run_schedule(self, build_rating_layer, build_perf_layer,
|
||
|
|
build_team_perf_layer, build_team_diff_layer,
|
||
|
|
build_trunc_layer, min_delta=DELTA):
|
||
|
|
"""Sends messages within every nodes of the factor graph until the
|
||
|
|
result is reliable.
|
||
|
|
"""
|
||
|
|
if min_delta <= 0:
|
||
|
|
raise ValueError('min_delta must be greater than 0')
|
||
|
|
layers = []
|
||
|
|
def build(builders):
|
||
|
|
layers_built = [list(build()) for build in builders]
|
||
|
|
layers.extend(layers_built)
|
||
|
|
return layers_built
|
||
|
|
# gray arrows
|
||
|
|
layers_built = build([build_rating_layer,
|
||
|
|
build_perf_layer,
|
||
|
|
build_team_perf_layer])
|
||
|
|
rating_layer, perf_layer, team_perf_layer = layers_built
|
||
|
|
for f in chain(*layers_built):
|
||
|
|
f.down()
|
||
|
|
# arrow #1, #2, #3
|
||
|
|
team_diff_layer, trunc_layer = build([build_team_diff_layer,
|
||
|
|
build_trunc_layer])
|
||
|
|
team_diff_len = len(team_diff_layer)
|
||
|
|
for x in range(10):
|
||
|
|
if team_diff_len == 1:
|
||
|
|
# only two teams
|
||
|
|
team_diff_layer[0].down()
|
||
|
|
delta = trunc_layer[0].up()
|
||
|
|
else:
|
||
|
|
# multiple teams
|
||
|
|
delta = 0
|
||
|
|
for x in range(team_diff_len - 1):
|
||
|
|
team_diff_layer[x].down()
|
||
|
|
delta = max(delta, trunc_layer[x].up())
|
||
|
|
team_diff_layer[x].up(1) # up to right variable
|
||
|
|
for x in range(team_diff_len - 1, 0, -1):
|
||
|
|
team_diff_layer[x].down()
|
||
|
|
delta = max(delta, trunc_layer[x].up())
|
||
|
|
team_diff_layer[x].up(0) # up to left variable
|
||
|
|
# repeat until to small update
|
||
|
|
if delta <= min_delta:
|
||
|
|
break
|
||
|
|
# up both ends
|
||
|
|
team_diff_layer[0].up(0)
|
||
|
|
team_diff_layer[team_diff_len - 1].up(1)
|
||
|
|
# up the remainder of the black arrows
|
||
|
|
for f in team_perf_layer:
|
||
|
|
for x in range(len(f.vars) - 1):
|
||
|
|
f.up(x)
|
||
|
|
for f in perf_layer:
|
||
|
|
f.up()
|
||
|
|
return layers
|
||
|
|
|
||
|
|
def rate(self, rating_groups, ranks=None, weights=None, min_delta=DELTA):
|
||
|
|
"""Recalculates ratings by the ranking table::
|
||
|
|
|
||
|
|
env = TrueSkill() # uses default settings
|
||
|
|
# create ratings
|
||
|
|
r1 = env.create_rating(42.222)
|
||
|
|
r2 = env.create_rating(89.999)
|
||
|
|
# calculate new ratings
|
||
|
|
rating_groups = [(r1,), (r2,)]
|
||
|
|
rated_rating_groups = env.rate(rating_groups, ranks=[0, 1])
|
||
|
|
# save new ratings
|
||
|
|
(r1,), (r2,) = rated_rating_groups
|
||
|
|
|
||
|
|
``rating_groups`` is a list of rating tuples or dictionaries that
|
||
|
|
represents each team of the match. You will get a result as same
|
||
|
|
structure as this argument. Rating dictionaries for this may be useful
|
||
|
|
to choose specific player's new rating::
|
||
|
|
|
||
|
|
# load players from the database
|
||
|
|
p1 = load_player_from_database('Arpad Emrick Elo')
|
||
|
|
p2 = load_player_from_database('Mark Glickman')
|
||
|
|
p3 = load_player_from_database('Heungsub Lee')
|
||
|
|
# calculate new ratings
|
||
|
|
rating_groups = [{p1: p1.rating, p2: p2.rating}, {p3: p3.rating}]
|
||
|
|
rated_rating_groups = env.rate(rating_groups, ranks=[0, 1])
|
||
|
|
# save new ratings
|
||
|
|
for player in [p1, p2, p3]:
|
||
|
|
player.rating = rated_rating_groups[player.team][player]
|
||
|
|
|
||
|
|
:param rating_groups: a list of tuples or dictionaries containing
|
||
|
|
:class:`Rating` objects.
|
||
|
|
:param ranks: a ranking table. By default, it is same as the order of
|
||
|
|
the ``rating_groups``.
|
||
|
|
:param weights: weights of each players for "partial play".
|
||
|
|
:param min_delta: each loop checks a delta of changes and the loop
|
||
|
|
will stop if the delta is less then this argument.
|
||
|
|
:returns: recalculated ratings same structure as ``rating_groups``.
|
||
|
|
:raises: :exc:`FloatingPointError` occurs when winners have too lower
|
||
|
|
rating than losers. higher floating-point precision couls
|
||
|
|
solve this error. set the backend to "mpmath".
|
||
|
|
|
||
|
|
.. versionadded:: 0.2
|
||
|
|
|
||
|
|
"""
|
||
|
|
rating_groups, keys = self.validate_rating_groups(rating_groups)
|
||
|
|
weights = self.validate_weights(weights, rating_groups, keys)
|
||
|
|
group_size = len(rating_groups)
|
||
|
|
if ranks is None:
|
||
|
|
ranks = range(group_size)
|
||
|
|
elif len(ranks) != group_size:
|
||
|
|
raise ValueError('Wrong ranks')
|
||
|
|
# sort rating groups by rank
|
||
|
|
by_rank = lambda x: x[1][1]
|
||
|
|
sorting = sorted(enumerate(zip(rating_groups, ranks, weights)),
|
||
|
|
key=by_rank)
|
||
|
|
sorted_rating_groups, sorted_ranks, sorted_weights = [], [], []
|
||
|
|
for x, (g, r, w) in sorting:
|
||
|
|
sorted_rating_groups.append(g)
|
||
|
|
sorted_ranks.append(r)
|
||
|
|
# make weights to be greater than 0
|
||
|
|
sorted_weights.append(max(min_delta, w_) for w_ in w)
|
||
|
|
# build factor graph
|
||
|
|
args = (sorted_rating_groups, sorted_ranks, sorted_weights)
|
||
|
|
builders = self.factor_graph_builders(*args)
|
||
|
|
args = builders + (min_delta,)
|
||
|
|
layers = self.run_schedule(*args)
|
||
|
|
# make result
|
||
|
|
rating_layer, team_sizes = layers[0], _team_sizes(sorted_rating_groups)
|
||
|
|
transformed_groups = []
|
||
|
|
for start, end in zip([0] + team_sizes[:-1], team_sizes):
|
||
|
|
group = []
|
||
|
|
for f in rating_layer[start:end]:
|
||
|
|
group.append(Rating(float(f.var.mu), float(f.var.sigma)))
|
||
|
|
transformed_groups.append(tuple(group))
|
||
|
|
by_hint = lambda x: x[0]
|
||
|
|
unsorting = sorted(zip((x for x, __ in sorting), transformed_groups),
|
||
|
|
key=by_hint)
|
||
|
|
if keys is None:
|
||
|
|
return [g for x, g in unsorting]
|
||
|
|
# restore the structure with input dictionary keys
|
||
|
|
return [dict(zip(keys[x], g)) for x, g in unsorting]
|
||
|
|
|
||
|
|
def quality(self, rating_groups, weights=None):
|
||
|
|
"""Calculates the match quality of the given rating groups. A result
|
||
|
|
is the draw probability in the association::
|
||
|
|
|
||
|
|
env = TrueSkill()
|
||
|
|
if env.quality([team1, team2, team3]) < 0.50:
|
||
|
|
print('This match seems to be not so fair')
|
||
|
|
|
||
|
|
:param rating_groups: a list of tuples or dictionaries containing
|
||
|
|
:class:`Rating` objects.
|
||
|
|
:param weights: weights of each players for "partial play".
|
||
|
|
|
||
|
|
.. versionadded:: 0.2
|
||
|
|
|
||
|
|
"""
|
||
|
|
rating_groups, keys = self.validate_rating_groups(rating_groups)
|
||
|
|
weights = self.validate_weights(weights, rating_groups, keys)
|
||
|
|
flatten_ratings = sum(map(tuple, rating_groups), ())
|
||
|
|
flatten_weights = sum(map(tuple, weights), ())
|
||
|
|
length = len(flatten_ratings)
|
||
|
|
# a vector of all of the skill means
|
||
|
|
mean_matrix = Matrix([[r.mu] for r in flatten_ratings])
|
||
|
|
# a matrix whose diagonal values are the variances (sigma ** 2) of each
|
||
|
|
# of the players.
|
||
|
|
def variance_matrix(height, width):
|
||
|
|
variances = (r.sigma ** 2 for r in flatten_ratings)
|
||
|
|
for x, variance in enumerate(variances):
|
||
|
|
yield (x, x), variance
|
||
|
|
variance_matrix = Matrix(variance_matrix, length, length)
|
||
|
|
# the player-team assignment and comparison matrix
|
||
|
|
def rotated_a_matrix(set_height, set_width):
|
||
|
|
t = 0
|
||
|
|
for r, (cur, _next) in enumerate(zip(rating_groups[:-1],
|
||
|
|
rating_groups[1:])):
|
||
|
|
for x in range(t, t + len(cur)):
|
||
|
|
yield (r, x), flatten_weights[x]
|
||
|
|
t += 1
|
||
|
|
x += 1
|
||
|
|
for x in range(x, x + len(_next)):
|
||
|
|
yield (r, x), -flatten_weights[x]
|
||
|
|
set_height(r + 1)
|
||
|
|
set_width(x + 1)
|
||
|
|
rotated_a_matrix = Matrix(rotated_a_matrix)
|
||
|
|
a_matrix = rotated_a_matrix.transpose()
|
||
|
|
# match quality further derivation
|
||
|
|
_ata = (self.beta ** 2) * rotated_a_matrix * a_matrix
|
||
|
|
_atsa = rotated_a_matrix * variance_matrix * a_matrix
|
||
|
|
start = mean_matrix.transpose() * a_matrix
|
||
|
|
middle = _ata + _atsa
|
||
|
|
end = rotated_a_matrix * mean_matrix
|
||
|
|
# make result
|
||
|
|
e_arg = (-0.5 * start * middle.inverse() * end).determinant()
|
||
|
|
s_arg = _ata.determinant() / middle.determinant()
|
||
|
|
return math.exp(e_arg) * math.sqrt(s_arg)
|
||
|
|
|
||
|
|
def expose(self, rating):
|
||
|
|
"""Returns the value of the rating exposure. It starts from 0 and
|
||
|
|
converges to the mean. Use this as a sort key in a leaderboard::
|
||
|
|
|
||
|
|
leaderboard = sorted(ratings, key=env.expose, reverse=True)
|
||
|
|
|
||
|
|
.. versionadded:: 0.4
|
||
|
|
|
||
|
|
"""
|
||
|
|
k = self.mu / self.sigma
|
||
|
|
return rating.mu - k * rating.sigma
|
||
|
|
|
||
|
|
def make_as_global(self):
|
||
|
|
"""Registers the environment as the global environment.
|
||
|
|
|
||
|
|
>>> env = TrueSkill(mu=50)
|
||
|
|
>>> Rating()
|
||
|
|
trueskill.Rating(mu=25.000, sigma=8.333)
|
||
|
|
>>> env.make_as_global() #doctest: +ELLIPSIS
|
||
|
|
trueskill.TrueSkill(mu=50.000, ...)
|
||
|
|
>>> Rating()
|
||
|
|
trueskill.Rating(mu=50.000, sigma=8.333)
|
||
|
|
|
||
|
|
But if you need just one environment, :func:`setup` is better to use.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return setup(env=self)
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
c = type(self)
|
||
|
|
if callable(self.draw_probability):
|
||
|
|
f = self.draw_probability
|
||
|
|
draw_probability = '.'.join([f.__module__, f.__name__])
|
||
|
|
else:
|
||
|
|
draw_probability = '%.1f%%' % (self.draw_probability * 100)
|
||
|
|
if self.backend is None:
|
||
|
|
backend = ''
|
||
|
|
elif isinstance(self.backend, tuple):
|
||
|
|
backend = ', backend=...'
|
||
|
|
else:
|
||
|
|
backend = ', backend=%r' % self.backend
|
||
|
|
args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma,
|
||
|
|
self.beta, self.tau, draw_probability, backend)
|
||
|
|
return ('%s(mu=%.3f, sigma=%.3f, beta=%.3f, tau=%.3f, '
|
||
|
|
'draw_probability=%s%s)' % args)
|
||
|
|
|
||
|
|
|
||
|
|
def rate_1vs1(rating1, rating2, drawn=False, min_delta=DELTA, env=None):
|
||
|
|
"""A shortcut to rate just 2 players in a head-to-head match::
|
||
|
|
|
||
|
|
alice, bob = Rating(25), Rating(30)
|
||
|
|
alice, bob = rate_1vs1(alice, bob)
|
||
|
|
alice, bob = rate_1vs1(alice, bob, drawn=True)
|
||
|
|
|
||
|
|
:param rating1: the winner's rating if they didn't draw.
|
||
|
|
:param rating2: the loser's rating if they didn't draw.
|
||
|
|
:param drawn: if the players drew, set this to ``True``. Defaults to
|
||
|
|
``False``.
|
||
|
|
:param min_delta: will be passed to :meth:`rate`.
|
||
|
|
:param env: the :class:`TrueSkill` object. Defaults to the global
|
||
|
|
environment.
|
||
|
|
:returns: a tuple containing recalculated 2 ratings.
|
||
|
|
|
||
|
|
.. versionadded:: 0.2
|
||
|
|
|
||
|
|
"""
|
||
|
|
if env is None:
|
||
|
|
env = global_env()
|
||
|
|
ranks = [0, 0 if drawn else 1]
|
||
|
|
teams = env.rate([(rating1,), (rating2,)], ranks, min_delta=min_delta)
|
||
|
|
return teams[0][0], teams[1][0]
|
||
|
|
|
||
|
|
|
||
|
|
def quality_1vs1(rating1, rating2, env=None):
|
||
|
|
"""A shortcut to calculate the match quality between just 2 players in
|
||
|
|
a head-to-head match::
|
||
|
|
|
||
|
|
if quality_1vs1(alice, bob) < 0.50:
|
||
|
|
print('This match seems to be not so fair')
|
||
|
|
|
||
|
|
:param rating1: the rating.
|
||
|
|
:param rating2: the another rating.
|
||
|
|
:param env: the :class:`TrueSkill` object. Defaults to the global
|
||
|
|
environment.
|
||
|
|
|
||
|
|
.. versionadded:: 0.2
|
||
|
|
|
||
|
|
"""
|
||
|
|
if env is None:
|
||
|
|
env = global_env()
|
||
|
|
return env.quality([(rating1,), (rating2,)])
|
||
|
|
|
||
|
|
|
||
|
|
def global_env():
|
||
|
|
"""Gets the :class:`TrueSkill` object which is the global environment."""
|
||
|
|
try:
|
||
|
|
global_env.__trueskill__
|
||
|
|
except AttributeError:
|
||
|
|
# setup the default environment
|
||
|
|
setup()
|
||
|
|
return global_env.__trueskill__
|
||
|
|
|
||
|
|
|
||
|
|
def setup(mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
|
||
|
|
draw_probability=DRAW_PROBABILITY, backend=None, env=None):
|
||
|
|
"""Setups the global environment.
|
||
|
|
|
||
|
|
:param env: the specific :class:`TrueSkill` object to be the global
|
||
|
|
environment. It is optional.
|
||
|
|
|
||
|
|
>>> Rating()
|
||
|
|
trueskill.Rating(mu=25.000, sigma=8.333)
|
||
|
|
>>> setup(mu=50) #doctest: +ELLIPSIS
|
||
|
|
trueskill.TrueSkill(mu=50.000, ...)
|
||
|
|
>>> Rating()
|
||
|
|
trueskill.Rating(mu=50.000, sigma=8.333)
|
||
|
|
|
||
|
|
"""
|
||
|
|
if env is None:
|
||
|
|
env = TrueSkill(mu, sigma, beta, tau, draw_probability, backend)
|
||
|
|
global_env.__trueskill__ = env
|
||
|
|
return env
|
||
|
|
|
||
|
|
|
||
|
|
def rate(rating_groups, ranks=None, weights=None, min_delta=DELTA):
|
||
|
|
"""A proxy function for :meth:`TrueSkill.rate` of the global environment.
|
||
|
|
|
||
|
|
.. versionadded:: 0.2
|
||
|
|
|
||
|
|
"""
|
||
|
|
return global_env().rate(rating_groups, ranks, weights, min_delta)
|
||
|
|
|
||
|
|
|
||
|
|
def quality(rating_groups, weights=None):
|
||
|
|
"""A proxy function for :meth:`TrueSkill.quality` of the global
|
||
|
|
environment.
|
||
|
|
|
||
|
|
.. versionadded:: 0.2
|
||
|
|
|
||
|
|
"""
|
||
|
|
return global_env().quality(rating_groups, weights)
|
||
|
|
|
||
|
|
|
||
|
|
def expose(rating):
|
||
|
|
"""A proxy function for :meth:`TrueSkill.expose` of the global environment.
|
||
|
|
|
||
|
|
.. versionadded:: 0.4
|
||
|
|
|
||
|
|
"""
|
||
|
|
return global_env().expose(rating)
|
||
|
|
|
||
|
|
|
||
|
|
# Append deprecated methods into :class:`TrueSkill` and :class:`Rating`
|
||
|
|
from . import deprecated # noqa
|
||
|
|
from .deprecated import ( # noqa
|
||
|
|
dynamic_draw_probability, match_quality, transform_ratings)
|
||
|
|
deprecated.ensure_backward_compatibility(TrueSkill, Rating)
|