# -*- coding: utf-8 -*-
"""
Implements coordinated PGT intelligence for SemiNFG object
Created on Tue Mar 12 17:38:37 2013
Copyright (C) 2013 James Bono (jwbono@gmail.com)
GNU Affero General Public License
"""
from __future__ import division
import copy
import numpy as np
from pynfg import DecisionNode, iterSemiNFG
from pynfg.utilities.utilities import mh_decision
[docs]def coordinated_MC(G, S, noise, X, M, innoise=1, delta=1, integrand=None, \
mix=False, satisfice=None):
"""Run MC outer loop on random strategy sequences for SemiNFG IQ calcs
:arg G: the semi-NFG to be evaluated
:type G: SemiNFG or iterSemiNFG
:arg S: number of policy profiles to sample
:type S: int
:type M: int
:arg noise: the degree of independence of the proposal distribution on the
current value. 1 is independent, 0 returns no perturbation.
:type noise: float
:arg X: number of samples of each policy profile
:type X: int
:arg M: number of random alt policies to compare
:arg innoise: the perturbation noise for the loop within iq_calc to draw
alt CPTs to compare utilities to current CPT.
:type innoise: float
:arg delta: the discount factor (ignored if SemiNFG)
:type delta: float
:arg integrand: a user-supplied function of G that is evaluated for each s
in S
:type integrand: func
:arg pure: True if restricting sampling to pure strategies. False if mixed
strategies are included in sampling. Default is True.
.. note::
This is the coordinated-approach because intelligence is assigned to a
player instead of being assigned to a DecisionNode
"""
intel = {} #keys are dn names, vals are iq time series
iq = {}
weight = {}
w = {}
funcout = {} #keys are s in S, vals are eval of integrand of G(s)
for s in xrange(1, S+1): #sampling S policy profiles
print s
GG = copy.deepcopy(G)
for p in GG.players:
w[p] = 1
for dn in GG.partition[p]: #drawing current policy
w[p] *= dn.perturbCPT(noise, mixed=mix, returnweight=True)
for p in GG.players: #find the iq of each player's policy in turn
iq[p] = coordinated_calciq(p, GG, X, M, mix, delta, innoise, \
satisfice)
if integrand is not None:
funcout[s] = integrand(GG) #eval integrand G(s), assign to funcout
intel[s] = copy.deepcopy(iq)
weight[s] = copy.deepcopy(w)
return intel, funcout, weight
[docs]def coordinated_MH(G, S, density, noise, X, M, innoise=1, delta=1, \
integrand=None, mix=False, satisfice=None):
"""Run MH outer loop on random strategy sequences for SemiNFG IQ calcs
:arg G: the semi-NFG to be evaluated
:type G: SemiNFG or iterSemiNFG
:arg S: number of MH iterations
:type S: int
:arg density: the function that assigns weights to iq
:type density: func
:arg noise: the degree of independence of the proposal distribution on the
current value. 1 is independent, 0 returns no perturbation.
:type noise: float
:arg X: number of samples of each policy profile
:type X: int
:arg M: number of random alt policies to compare
:type M: int
:arg innoise: the perturbation noise for the loop within iq_calc to draw
alt CPTs to compare utilities to current CPT.
:type innoise: float
:arg delta: the discount factor (ignored if SemiNFG)
:type delta: float
:arg integrand: a user-supplied function of G that is evaluated for each s
in S
:type integrand: func
:arg mix: if true, proposal distribution is over mixed CPTs. Default is
False.
:type mix: bool
.. note::
This is the coordinated-approach because intelligence is assigned to a
player instead of being assigned to a DecisionNode
"""
intel = {} #keys are s in S, vals are iq dict (dict of dicts)
iq = {} #keys are base names, iq timestep series
funcout = {} #keys are s in S, vals are eval of integrand of G(s)
dens = np.zeros(S+1) #storing densities for return
for s in xrange(1, S+1): #sampling S sequences of policy profiles
print s
GG = copy.deepcopy(G)
for p in GG.players:
for dn in GG.partition[p]: #drawing current policy
dn.perturbCPT(noise, mixed=mix, setCPT=False)
for p in GG.players:#getting iq
iq[p] = coordinated_calciq(p, GG, X, M, mix, delta, innoise, \
satisfice)
# The MH decision
current_dens = density(iq)
verdict = mh_decision(current_dens, dens[s-1])
if verdict: #accepting new CPT
intel[s] = copy.deepcopy(iq)
G = copy.deepcopy(GG)
dens[s] = current_dens
else:
intel[s] = intel[s-1]
dens[s] = dens[s-1]
if integrand is not None:
funcout[s] = integrand(G) #eval integrand G(s), assign to funcout
return intel, funcout, dens[1::]
[docs]def coordinated_calciq(p, G, X, M, mix, delta, innoise, satisfice=None):
"""Calc IQ of player p in G across all of p's decision nodes
:arg p: the name of the player whose intelligence is being evaluated.
:type p: str
:arg G: the semi-NFG to be evaluated
:type G: SemiNFG or iterSemiNFG
:arg X: number of samples of each policy profile
:type X: int
:arg M: number of random alt policies with which to compare
:type M: int
:arg mix: if true, proposal distribution is over mixed CPTs. Default is
False.
:type mix: bool
:arg delta: the discount factor (ignored if SemiNFG)
:type delta: float
:arg innoise: the perturbation noise for the inner loop to draw alt CPTs
:type innoise: float
:returns: the fraction of alternative policies that have a lower npv
reward than the current policy.
"""
util = 0
altutil = [0]*M
weight = np.ones(M)
tick = 0
if isinstance(G, iterSemiNFG):
ufoo = G.npv_reward
uargs = [p, G.starttime, delta]
else:
ufoo = G.utility
uargs = [p]
for x in xrange(1,X+1):
G.sample()
util = (ufoo(*uargs)+(x-1)*util)/x
if satisfice: #using the satisficing distribution for drawing alternatives
G = satisfice
for m in range(M): #Sample M alt policies for the player
GG = copy.deepcopy(G)
for dn in GG.partition[p]: #rand CPT for the DN
#density for the importance sampling distribution
if innoise == 1 or satisfice:
dn.perturbCPT(innoise, mixed=mix)
denw=1
else:
denw = dn.perturbCPT(innoise, mixed=mix, returnweight=True)
if not tick:
numw = denw #scaling constant num to ~ magnitude of den
weight[m] *= (numw/denw)
tick += 1
GG.sample() #sample altpolicy prof. to end of net
if isinstance(GG, iterSemiNFG):
altutil[m] = GG.npv_reward(p, GG.starttime, delta)
else:
altutil[m] = GG.utility(p)
#weight of alts worse than G
worse = [weight[m] for m in range(M) if altutil[m]<util]
return np.sum(worse)/np.sum(weight) #fraction of alts worse than G is IQ