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model.py
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import pylab
from time import time
import numpy as np
import sys
from regrivermod import *
from econlearn import Qlearn
from results.chartbuilder import *
class Model:
def __init__(self, para, ch7=False, turn_off_env=False):
pylab.ioff()
################# Create objects #################
print '\nDecentralised storage model with ' + str(para.N) + ' users. \n'
self.para = para
self.storage = Storage(para, ch7)
self.users = Users(para, ch7)
self.sim = Simulation(para, ch7)
if ch7:
self.env = Environment(para, turn_off=turn_off_env)
self.market = Market(para, self.users)
self.utility = Utility(self.users, self.storage, para, ch7, self.env)
self.learn_market_demand()
else:
self.utility = Utility(self.users, self.storage, para)
print '----------------------------------------------------'
print 'Sum of user shares: ' + str(np.sum(self.utility.c_F))
print 'User shares: ' + str(np.array(self.utility.c_F))
print '----------------------------------------------------'
def plannerSDP(self, seed=0, plot=False):
SR = self.utility.sr
self.utility.sr = -1
################# Solve planners problem #################
print 'Solving the planner\'s problem...'
self.sdp = SDP(self.para.SDP_GRID, self.users, self.storage, self.para)
tic = time()
self.sdp.policy_iteration(self.para.SDP_TOL, self.para.SDP_ITER, plot=plot)
toc = time()
st = toc - tic
print 'Solve time: ' + str(st)
self.sim.simulate(self.users, self.storage, self.utility, self.para.T0, self.para.CPU_CORES, planner=True, policy=True, polf=self.sdp.W_f, delta=0, stats=True, planner_explore=False, t_cost_off=True, seed=seed)
self.utility.sr = SR
self.p_series = self.sim.series
return [self.sim.stats, self.sdp, st]
def simulate_myopic(self, simT, seed=0):
SR = self.utility.sr
self.utility.sr = -1
#self.sim.ITER = 0
self.sim.simulate(self.users, self.storage, self.utility, simT, self.para.CPU_CORES, planner=True, policy=False, polf=0, delta=0, stats=True, planner_explore=False, t_cost_off=True, seed=seed, myopic=True)
self.utility.sr = SR
self.series = self.sim.series
return [self.sim.stats['SW']['Mean'][0], self.sim.stats['S']['Mean'][0]]
def simulate_SOP(self, simT, Sbar, Lambda, seed=0):
self.para.central_case()
self.para.set_property_rights('RS-HL')
self.para.Lambda_high = Lambda
self.storage = Storage(self.para, False)
self.users = Users(self.para)
self.sim = Simulation(self.para)
self.utility = Utility(self.users, self.storage, self.para)
self.sim.ITER = 0
self.sim.simulate(self.users, self.storage, self.utility, simT, self.para.CPU_CORES, planner=True, policy=False, polf=0, delta=0, stats=True, planner_explore=False, t_cost_off=False, seed=seed, myopic=False, SOP=True, Sbar=Sbar)
SW = self.sim.stats['SW']['Mean'][0]
U_low = self.sim.stats['U_low']['Mean'][0]
U_high = self.sim.stats['U_high']['Mean'][0]
yield_low = self.sim.stats['A_low']['Mean'][0] / self.sim.stats['A_low']['Max'][0]
yield_high = self.sim.stats['A_high']['Mean'][0] / self.sim.stats['A_high']['Max'][0]
SD_low = self.sim.stats['A_low']['SD'][0] / self.sim.stats['A_low']['Mean'][0]
SD_high = self.sim.stats['A_high']['SD'][0] / self.sim.stats['A_high']['Mean'][0]
P_low = np.sum((self.sim.series['P'] * self.sim.series['A_low']) * (self.para.beta**np.arange(simT))) / self.sim.stats['A_low']['Max'][0]
P_high = np.sum((self.sim.series['P'] * self.sim.series['A_high']) * (self.para.beta**np.arange(simT))) / self.sim.stats['A_high']['Max'][0]
return [SW, U_low, U_high, yield_low, yield_high, SD_low, SD_high, P_low, P_high]
def plannerQV(self, t_cost_off=True, T1=200000, T2=400000, stage1=True, stage2=True, d=0, seed=0, type='ASGD', simulate=True):
if type == 'A':
Ta = [11, 11, 7]
La = 12
Tb = [6, 11, 7]
Lb = 20
minsamp = int(60 * (T1 / 100000)**0.5)
asgd = False
elif type == 'ASGD':
Ta = [6, 5, 4]
La = 25
Tb = Ta
Lb = La
minsamp = 1
asgd = True
tic = time()
SR = self.utility.sr
self.utility.sr = -1
# Feasibility constraints
Alow = lambda X: 0 # W > 0
Ahigh = lambda X: X[0] # W < S
D = 2
xargs=['x', 1]
if stage1:
self.sim.simulate(self.users, self.storage, self.utility, T1, self.para.CPU_CORES, planner=True, policy=False, polf=self.sdp.W_f,
delta=0, stats=False, planner_explore=True, t_cost_off=t_cost_off)
if type=='RF':
self.qv = Qlearn.QVtree(D, self.para.s_points1, self.para.s_radius1, self.para, num_split=40, num_leaf=20, num_est=215)
else:
self.qv = Qlearn.QVtile(D, Ta, La, 1, minsamp, self.para.sg_radius1, self.para, asgd=asgd, linT=8)
self.qv.iterate(self.sim.XA_t, self.sim.X_t1, self.sim.series['SW'], Alow, Ahigh, ITER=self.para.QV_ITER1, Ascaled=False,
plot=False, xargs=xargs, eta=0.8, sg_points=self.para.sg_points1, maxT=250000)
XA = self.sim.XA_t
X = self.sim.X_t1
SW = self.sim.series['SW']
if stage2:
self.qv.resetQ(D, Tb, Lb, 1, minsamp)
self.sim.simulate(self.users, self.storage, self.utility, T2, self.para.CPU_CORES, planner=True, policy=True, polf=self.qv.W_f,
delta=d, stats=False, planner_explore=True, t_cost_off=t_cost_off)
if not(stage1):
XA = self.sim.XA_t
X = self.sim.X_t1
SW = self.sim.series['SW']
else:
XA = np.vstack([XA, self.sim.XA_t])
X = np.vstack([X, self.sim.X_t1])
SW = np.hstack([SW, self.sim.series['SW']])
self.qv.iterate(XA, X, SW, Alow, Ahigh, ITER=self.para.QV_ITER2, Ascaled=False,
plot=False, xargs=xargs, eta=0.8, sg_points=self.para.sg_points1, maxT=250000)
toc = time()
st = toc - tic
print 'Solve time: ' + str(st)
if simulate:
self.sim.simulate(self.users, self.storage, self.utility, self.para.T0, self.para.CPU_CORES, planner=True, policy=True, polf=self.qv.W_f, delta=0, stats=True, planner_explore=False, t_cost_off=t_cost_off, seed=seed)
self.utility.sr = SR
return [self.sim.stats, self.qv, st]
def learn_market_demand(self, T=100000):
self.utility.init_policy(self.storage, self.para)
self.utility.explore = 1
self.utility.d = 0
self.sim.simulate_ch7(self.users, self.storage, self.utility, self.market, self.env, T, self.para.CPU_CORES , planner=True, stats=True, initP=True)
self.market.estimate_market_demand(self.sim.series['P'][:, 0], self.sim.series['A'][:, 0], self.sim.series['Q_low'][:, 0], self.sim.series['Q_high'][:, 0], self.sim.series['Q_env'][:, 0], self.sim.series['Bhat'][:, 0], self.sim.series['I'][:, 0]*(self.storage.I_bar_ch7[0]**-1), self.para)
def plannerQV_ch7(self, t_cost_off=True, T=250000, stage2=False, d=0, simulate=True, envoff=False):
tic = time()
if envoff:
Tiles = [[6, 5, 4], [6,5,4]]
D = 2
a = [0, 0, 0.25]
b = [100, 100, 99.75]
pc_samp = 0.5
xarg = ['x', 1]
else:
Tiles = [[6, 6, 6, 5], [6, 6, 6, 5]]
D = 3
xarg = ['x', 1, 1]
a = [0, 0, 0.25, 0]
b = [100, 100, 99.75, 100]
pc_samp = 0.5
L = 25
# Feasibility constraints
Alow = lambda X: 0 # W > 0
Ahigh = lambda X: X[0] # W < S
self.utility.init_policy(self.storage, self.para)
self.utility.explore = 1
self.utility.d = 0
self.sim.simulate_ch7(self.users, self.storage, self.utility, self.market, self.env, T, self.para.CPU_CORES, planner=True)
self.qv = Qlearn.QVtile_ch7(D, Tiles, L, 1, 1, self.para.sg_radius1_ch7, self.para, asgd=True, linT=7)
self.qv.iterate(self.sim.XA, self.sim.X1, self.sim.U, Alow, Ahigh, ITER=self.para.QV_ITER1,
plot=False, eta=0.8, sg_points=self.para.sg_points1_ch7/2, maxT=250000, a=a, b=b, pc_samp=pc_samp, plotiter=False, xargs=xarg)
#for m in range(2):
# self.qv.W_f[m].plot(xarg)
# pylab.show()
# self.qv.V_f[m].plot(xarg)
# pylab.show()
self.utility.policy0 = self.qv.W_f[0]
self.utility.policy1 = self.qv.W_f[1]
if stage2:
XA = self.sim.XA
X = self.sim.X1
SW = self.sim.U
self.qv.resetQ(D, Tiles, L, 1, 1)
self.utility.d = d
self.sim.simulate_ch7(self.users, self.storage, self.utility, self.market, self.env, T, self.para.CPU_CORES, planner=True)
XA = np.vstack([XA, self.sim.XA])
X = np.vstack([X, self.sim.X1])
SW = np.hstack([SW, self.sim.U])
self.qv.iterate(XA, X, SW, Alow, Ahigh, ITER=self.para.QV_ITER2, Ascaled=False,
plot=False, eta=0.8, sg_points=self.para.sg_points1_ch7, maxT=250000, a=a, b=b, pc_samp=pc_samp)
self.utility.policy = self.qv.W_f
toc = time()
st = toc - tic
print 'Solve time: ' + str(st)
if simulate:
self.utility.explore = 0
self.sim.simulate_ch7(self.users, self.storage, self.utility, self.market, self.env, self.para.T0, self.para.CPU_CORES, planner=True, stats=True)
return [self.sim.stats, self.qv, st]
def chapter7_initialise(self, ):
self.para.sg_radius1_ch7 = 0.02
self.para.sg_points1_ch7 = 750
stats, _, _ = self.plannerQV_ch7(T=200000, stage2=False, d=0.2, simulate=True, envoff=True)
E_cons = stats['E']['Summer']['Mean'][self.sim.ITEROLD]
self.env.turn_off = 0
self.sim.ITER = 0
self.sim.ITERNEW = 0
self.sim.ITEROLD = 0
self.para.sg_radius1_ch7 = 0.045
self.para.sg_points1_ch7 = 2500
stats, _, _ = self.plannerQV_ch7(T=200000, stage2=False, d=0.2, simulate=True, envoff=False)
E_opt = stats['E']['Summer']['Mean'][self.sim.ITEROLD]
E_lambda = 1 - E_opt / E_cons
print 'E_cons: ' + str(E_cons) + ', E_opt: ' + str(E_opt) + ', % change: ' + str(E_lambda)
return E_lambda
def chapter7(self, P_adj, psearch=True):
from econlearn.tilecode import Tilecode as Tile
self.plannerQV_ch7(T=200000, stage2=False, d=0.2, simulate=True, envoff=False)
if self.para.sr == 'NS':
NS = True
else:
NS = False
big_tic = time()
################## User starting values #################
print 'Starting values, fitted QV iteration ...'
self.users.set_explorers(2, self.para.d[0])#.3)
self.env.explore = 1
self.env.d = 0.3
#stats, qv =
self.multiQV_ch7(ITER=self.para.ITER1, init=True, NS=NS)
################## Main Q-learning #################
print '\nSolve decentralised problem, multiple agent fitted QV iteration ...'
counter = 0
scale = 1
P_adj_plot = np.zeros(self.para.ITER2)
self.env.P_adj = P_adj
self.market.P_adj = P_adj
for i in range(self.para.ITER2):
print '\n --- Iteration: ' + str(i) + ' ---\n'
print '-----------------------------------------'
stats, qv = self.multiQV_ch7(ITER=1, partial=True, NS=NS)
self.users.update_policy_ch7(qv[0].W_f, qv[1].W_f, Np=self.para.update_rate_ch7[i], N_e=2, d=self.para.d[i])
self.env.update_policy(qv[2].W_f)
self.env.d = self.para.envd[i]
print '------- Summer --------'
print 'Mean A low: ' + str(np.mean(self.sim.series['A_low'][:,0]))
print 'Mean A high: ' + str(np.mean(self.sim.series['A_high'][:,0]))
print 'Mean A env: ' + str(np.mean(self.sim.series['A_env'][:,0]))
print 'Mean Q low: ' + str(np.mean(self.sim.series['Q_low'][:,0]))
print 'Mean Q high: ' + str(np.mean(self.sim.series['Q_high'][:,0]))
print 'Mean Q env: ' + str(np.mean(self.sim.series['Q_env'][:,0]))
print 'Mean EWH budget outcome: ' + str(np.mean(self.sim.series['Budget'][:, 0]))
print '------- Winter --------'
print 'Mean A low: ' + str(np.mean(self.sim.series['A_low'][:,1]))
print 'Mean A high: ' + str(np.mean(self.sim.series['A_high'][:,1]))
print 'Mean A env: ' + str(np.mean(self.sim.series['A_env'][:,1]))
print 'Mean Q low: ' + str(np.mean(self.sim.series['Q_low'][:,1]))
print 'Mean Q high: ' + str(np.mean(self.sim.series['Q_high'][:,1]))
print 'Mean Q env: ' + str(np.mean(self.sim.series['Q_env'][:,1]))
print 'Mean EWH budget outcome: ' + str(np.mean(self.sim.series['Budget'][:, 1]))
if psearch:
counter += 1
if counter > 3:
counter = 0
self.users.exploring = 0
self.env.explore = 0
P_adj_sim, Budget_sim = self.sim.simulate_ch7(self.users, self.storage, self.utility, self.market, self.env, 100000,self.para.CPU_CORES, stats=True, budgetonly=True)
self.users.exploring = 1
self.env.explore = 1
approx = Tile(1, [11], 30, min_sample=120)
approx.fit(P_adj_sim, Budget_sim)
#approx.plot()
#pylab.show()
X = np.linspace(P_adj - 2.5*30, P_adj + 2.5*30, 1000).reshape([1000, 1])
Y = approx.predict(X)
idx = np.abs(Y) > 0
idx2 = np.argmin(np.abs(Y[idx]))
P_adj2 = X[idx][idx2]
Y2 = Y[idx][idx2]
Y1 = Y[500]
if Y2 > 6000000: # linear extrapolation
P_adj = P_adj - 50 #*(abs(Y2)/5000000)# Y1 * ((P_adj - P_adj2) / (Y1 - Y2))
elif Y2 < -6000000:
P_adj = P_adj + 50 #*(abs(Y2)/5000000)
else:
P_adj = P_adj2
self.env.P_adj = P_adj
self.market.P_adj = P_adj
print '======================================================'
print 'P_adj: ' + str(self.market.P_adj)
print 'Budget hat: ' + str(Y2)
print '======================================================'
#pylab.plot(X, Y)
#pylab.show()
#import pdb; pdb.set_trace()
#P_adj_plot[i] = P_adj
#pylab.plot(P_adj_plot)
#pylab.show()
"""
counter += 1
if counter > 7:
budget = np.mean(self.sim.series['Budget'])
#self.users.exploring = 0
#self.env.explore = 0
#budget = self.sim.simulate_ch7(self.users, self.storage, self.utility, self.market, self.env, 100000,self.para.CPU_CORES, stats=True, budgetonly=True)
#self.users.exploring = 1
#self.env.explore = 1
if budget > 0:
P_adj -= delta * scale
else:
P_adj += delta * scale
self.env.P_adj = P_adj
self.market.P_adj = P_adj
print 'P_adj: ' + str(self.market.P_adj)
print 'Budget: ' + str(budget)
counter = 0
scale *= 0.79
"""
if psearch and self.para.sr == 'OA':
print '============================ Final stage ======================================'
self.users.exploring = 0
self.env.explore = 0
iters = 0
sign = 1
delta = 1
while iters < 75:
self.sim.simulate_ch7(self.users, self.storage, self.utility, self.market, self.env, 100000, self.para.CPU_CORES, partial=False, stats=True)
budget = np.mean(self.sim.series['Budget'])
if abs(budget) < 100000:
print '======================================================'
print 'P_adj: ' + str(self.market.P_adj)
print 'Budget hat: ' + str(budget)
print '======================================================'
iters += 201
break
else:
if budget > 0:
P_adj -= 20*delta
if sign == -1:
delta = 0.8*delta
sign = 1
else:
P_adj += 20
if sign == 1:
delta = 0.8*delta
sign = -1
self.env.P_adj = P_adj
self.market.P_adj = P_adj
print '======================================================'
print 'P_adj: ' + str(self.market.P_adj)
print 'Budget hat: ' + str(budget)
print '======================================================'
iters += 1
if psearch:
self.users.exploring = 0
self.env.explore = 0
P_adj_sim, Budget_sim = self.sim.simulate_ch7(self.users, self.storage, self.utility, self.market, self.env, 500000,self.para.CPU_CORES, stats=True, budgetonly=True)
approx = Tile(1, [11], 30, min_sample=120)
approx.fit(P_adj_sim, Budget_sim)
X = np.linspace(P_adj - 2.5*30, P_adj + 2.5*30, 1000).reshape([1000, 1])
Y = approx.predict(X)
idx = np.abs(Y) > 0
idx2 = np.argmin(np.abs(Y[idx]))
P_adj2 = X[idx][idx2]
Y2 = Y[idx][idx2]
Y1 = Y[500]
P_adj = P_adj2
self.env.P_adj = P_adj
self.market.P_adj = P_adj
print '======================================================'
print 'Last chance'
print 'P_adj: ' + str(self.market.P_adj)
print 'Budget hat: ' + str(Y2)
print '======================================================'
self.users.exploring = 0
self.env.explore = 0
self.sim.simulate_ch7(self.users, self.storage, self.utility, self.market, self.env, self.para.T2, self.para.CPU_CORES, partial=False, stats=True)
big_toc = time()
print "Total time (minutes): " + str(round((big_toc - big_tic) / 60, 2))
#return [self.sim.stats, P_adj, self.sim.series]
return [self.sim.stats, P_adj]
def multiQV_ch7(self, ITER, init=False, eta=0.7, partial=False, NS=False):
tic = time()
if init:
Tuser = [[6, 5, 6, 3, 4], [6, 5, 6, 1, 3]]
Luser = 25
minsamp = 3
asgd = True
Tenv = [[5, 4, 5, 4, 4], [5, 4, 5, 4, 4]]
Lenv = 25
for m in range(2):
self.users.init_policy_ch7(self.qv.W_f[m], self.qv.V_f[m], self.storage, self.utility, self.para.linT, self.para.CPU_CORES,
self.para.sg_radius2_ch7, m)
self.env.init_policy(self.qv.W_f[m], self.qv.V_f[m], self.storage, self.utility, self.para.linT, self.para.CPU_CORES,
self.para.sg_radius2_ch7, m)
self.qv_multi = [0, 0, 0]
self.qv_multi[0] = Qlearn.QVtile_ch7(4, Tuser, Luser, 0.5, minsamp, self.para.sg_radius2_ch7, self.para, asgd=asgd,
linT=self.para.linT)
self.qv_multi[1] = Qlearn.QVtile_ch7(4, Tuser, Luser, 0.5, minsamp, self.para.sg_radius2_ch7, self.para, asgd=asgd,
linT=self.para.linT)
self.qv_multi[2] = Qlearn.QVtile_ch7(4, Tenv, Lenv, 0.5, minsamp, self.para.sg_radius2_ch7, self.para, asgd=asgd,
linT=self.para.linT)
if partial:
bigT = int((self.para.T2_ch7 / 2) * self.para.sample_rate)
else:
bigT = int(self.para.T2_ch7 / 2)
self.sim.simulate_ch7(self.users, self.storage, self.utility, self.market, self.env, bigT,self.para.CPU_CORES, partial=partial, stats=False) #
# Feasibility constraints
Alow = lambda X: 0 # w > 0
Ahigh = lambda X: X[1] # w < s
print "\nSolving low reliability users problem"
print "-------------------------------------\n"
self.qv_multi[0].iterate([self.sim.XA_t[0], self.sim.XA_t1[0]], [self.sim.X_t1[0], self.sim.X_t11[0]], [self.sim.u_t[0], self.sim.u_t1[0]],
Alow, Ahigh, ITER=ITER, a = [0, 0, 0, 0.25, 0.25],b = [100, 100, 100, 99.40, 99.40], pc_samp=0.25,
maxT=1200000,eta=eta, tilesg=True, sg_samp=self.para.sg_samp2_ch7, sg_prop=self.para.sg_prop2_ch7, sgmem_max=0.15, plotiter=False,
xargs=[300000,'x', 1, 1, 1], test=False, plot=False, NS=NS)
print "\nSolving high reliability users problem"
print "-------------------------------------\n"
self.qv_multi[1].iterate([self.sim.XA_t[1], self.sim.XA_t1[1]], [self.sim.X_t1[1], self.sim.X_t11[1]], [self.sim.u_t[1], self.sim.u_t1[1]],
Alow, Ahigh, ITER=ITER, a = [0, 0, 0, 0.25, 0.25], b = [100, 100, 100, 99.40, 99.40], pc_samp=0.25,
maxT=1200000, eta=eta, tilesg=True, sg_samp=self.para.sg_samp2_ch7, sg_prop=self.para.sg_prop2, sgmem_max=0.15, plotiter=False,
xargs=[300000,'x', 1, 1, 1], plot=False, NS=NS)
print "\nSolving the EWHs problem"
print "-------------------------------------\n"
self.qv_multi[2].iterate(self.sim.XA_e, self.sim.X1_e, self.sim.u_e, Alow, Ahigh, ITER=ITER, maxT=600000, eta=eta,
tilesg=True, sg_samp=self.para.sg_samp2_ch7, sg_prop=self.para.sg_prop2, sgmem_max= 0.15, plotiter=False, xargs=[1000000,'x', 1, 1], plot=False, NS=NS)
toc = time()
st = toc - tic
print 'Total time: ' + str(st)
return [self.sim.stats, self.qv_multi]
def multiQV(self, ITER, init=False, type='ASGD', eta=0.7, testing=False, test_idx=0, partial=False, NS=False):
tic = time()
if type == 'A':
Ta = [10, 5, 10, 5, 5]
La = 12
Tb = [6, 5, 11, 5, 5]
Lb = 20
minsamp = 50
asgd = False
elif type == 'ASGD':
Ta = [6, 5, 6, 5, 4]
La = 25
Tb = [5, 5, 6, 4, 4]
Lb = 25
minsamp = 3
asgd = True
if init:
self.HL = [0, 1]
self.users.testing = 0
w_f, v_f = self.users.init_policy(self.sdp.W_f, self.sdp.V_f, self.storage, self.para.linT, self.para.CPU_CORES, self.para.sg_radius2)
if not(partial):
self.qvHL = [0, 0]
for h in self.HL:
self.qvHL[h] = Qlearn.QVtile(4, Ta, La, 1, minsamp, self.para.sg_radius2, self.para, asgd=asgd, linT=self.para.linT, init=True, W_f=w_f[h], V_f=v_f[h])
if testing:
bigT = self.para.T2
elif partial:
bigT = int((self.para.T2 / self.users.N_e) * self.para.sample_rate)
else:
bigT = int(self.para.T2 / self.users.N_e)
self.sim.simulate(self.users, self.storage, self.utility, bigT, self.para.CPU_CORES, stats=False, partial=partial)
# Feasibility constraints
Alow = lambda X: 0 # w > 0
Ahigh = lambda X: X[1] # w < s
for h in self.HL:
self.qvHL[h].iterate(self.sim.XA_t[h], self.sim.X_t1[h], self.sim.u_t[h], Alow, Ahigh, ITER=ITER, Ascaled=False, plot=False, xargs=[1000000, 'x', 1, 2.0], a = [0, 0, 0, 0.25, 0.25], b = [100, 100, 100, 99.75, 100], pc_samp=0.25, maxT=500000, eta=eta, tilesg=True, sg_samp=self.para.sg_samp2, sg_prop=self.para.sg_prop2, NS=NS)
toc = time()
st = toc - tic
print 'Total time: ' + str(st)
return [self.sim.stats, self.qvHL]
def plannerQVTree(self, t_cost_off=True, T1=200000, T2=400000, simT=800000, stage1=True, stage2=True, d=0.15, seed=0):
tic = time()
self.utility.sr = -1
# Feasibility constraints
Alow = lambda X: 0 # W > 0
Ahigh = lambda X: X[0] # W < S
if stage1:
self.sim.simulate(self.users, self.storage, self.utility, T1, self.para.CPU_CORES, planner=True, policy=False,
delta=0, stats=False, planner_explore=True, t_cost_off=t_cost_off)
self.qv = Qlearn.QVtree(2, self.para.s_points1, self.para.s_radius1, self.para)
self.qv.iterate(self.sim.XA_t, self.sim.X_t1, self.sim.series['SW'], Alow, Ahigh, ITER=10, Ascaled=False, plot=True)
if stage2:
self.sim.simulate(self.users, self.storage, self.utility, T2, self.para.CPU_CORES, planner=True, policy=True,
polf=self.qv.W_f, delta=d, stats=False, planner_explore=True, t_cost_off=t_cost_off)
# Search range
Alow = lambda X, Ws: max(Ws - X[0]*d , 0) # W > W* - d*S
Ahigh = lambda X, Ws: min(Ws + X[0]*d, X[0]) # W < W* + d*S
self.qv.iterate(self.sim.XA_t, self.sim.X_t1, self.sim.series['SW'], Alow, Ahigh, ITER=5, Ascaled=True, plot=True)
toc = time()
st = toc - tic
print 'Solve time: ' + str(st)
self.sim.ITER = 0
self.sim.simulate(self.users, self.storage, self.utility, simT, self.para.CPU_CORES, planner=True, policy=True,
polf=self.qv.W_f, delta=0, stats=True, planner_explore=False, t_cost_off=t_cost_off, seed=seed)
return [self.sim.stats, self.qv, st]
def chapter6(self, sens=False):
print '\n --- Scenario --- \n'
print str(self.para.sr) + ' storage rights, loss deductions = ' + str(self.para.ls) + ', priority = ' + str(self.para.HL) + '\n'
home = '/home/nealbob'
folder = '/Dropbox/model/results/chapter6/'
out = '/Dropbox/Thesis/IMG/chapter6/'
img_ext = '.pdf'
big_tic = time()
# Planners problem
p_stats, p_sdp, p_st = self.plannerSDP()
################# Solve Release sharing problem #################
# Search for optimal shares by stochastic hill climbing
print '\n Solve release sharing problem... '
if sens:
self.users.set_shares(self.para.Lambda_high_RS)
self.utility.set_shares(self.para.Lambda_high_RS, self.users)
stats, qv, st = self.plannerQV(t_cost_off=False, stage1=True, stage2=True, T1=self.para.T1, T2=self.para.T1, d=self.para.policy_delta, simulate=True)
ITER = 0
if self.para.opt_lam == 1:
print 'Search for optimal shares... \n'
ITER = self.para.opt_lam_ITER
delta = self.para.Lambda_high / 1.5
SW = np.zeros(ITER + 1)
SW[0] = self.sim.stats['SW']['Mean'][1]
Lambda = np.zeros(ITER + 1)
Lambda[0] = self.para.Lambda_high
SW_max = SW[0]
Lambda_max = self.para.Lambda_high
else:
Lambda_max = self.utility.Lambda_high
for i in range(1, ITER + 1):
Lambda[i] = max(min(Lambda_max + np.random.rand() * delta, 0.99), 0.01)
self.users.set_shares(Lambda[i])
self.utility.set_shares(Lambda[i], self.users)
stats, qv, st = self.plannerQV(t_cost_off=False, stage1=True, stage2=True, T1=self.para.T1, T2=self.para.T1, d=self.para.policy_delta)
SW[i] = self.sim.stats['SW']['Mean'][self.sim.ITEROLD]
if SW[i] > SW_max:
delta *= 0.8
Lambda_max = Lambda[i]
SW_max = SW[i]
else:
delta *= -0.8
print '--- Optimal share search ---'
print 'Lambda: ' + str(Lambda[i])
print 'Welfare: ' + str(SW[i])
print 'Best Lambda: ' + str(Lambda_max)
print 'Best welfare: ' + str(SW_max)
#if self.para.opt_lam:
#data = [[Lambda, SW]]
#chart = {'OUTFILE': home + out + 'Lambdatest' + str(self.para.HL) + img_ext,
# 'YLABEL': 'Mean welfare',
# 'XLABEL': 'High reliability user inflow share' }
#build_chart(chart, data, chart_type='scatter')
self.RSLambda = Lambda_max
################# Solve storage right problem #################
if self.utility.sr >= 0:
if sens:
self.users.set_shares(self.para.Lambda_high)
self.utility.set_shares(self.para.Lambda_high, self.users)
temp = self.para.ITER2
#self.sim.ITER = 1
#self.sim.ITEROLD = 0
#self.sim.ITERNEW = 1
### ============================
if self.para.opt_lam:
if self.para.unbundled:
self.para.ITER2 = 140
else:
self.para.ITER2 = 100
n = np.count_nonzero(self.sim.stats['SW']['Mean']) - 1
# Optimal inflow shares
SW = np.zeros(self.para.ITER2)
SW[0] = self.sim.stats['SW']['Mean'][n]
SW_max = SW[0]
Lambda = np.zeros(self.para.ITER2)
Lambda[0] = Lambda_max
delta = Lambda[0] / 10
LambdaK = np.zeros(self.para.ITER2)
LambdaK[0] = Lambda_max
deltaK = delta
j = 0
counter = 0
Kchange = False
# ===============================
################## User starting values #################
print 'User starting values, fitted Q-iteration ...'
self.users.set_explorers(self.para.N_e[0], self.para.d[0])
stats, qv = self.multiQV(ITER=self.para.ITER1, init=True, type='ASGD')
################## Main Q-learning #################
V_e = np.zeros([self.para.ITER2, 2]) # Value error
P_e = np.zeros([self.para.ITER2, 2]) # Policy error
print '\nSolve decentralised problem, multiple agent fitted Q-iteration ...'
for i in range(self.para.ITER2):
print '\n --- Iteration: ' + str(i) + ' ---\n'
print 'Number of Explorers: '+ str(self.para.N_e[i] * 2) + ' of ' + str(self.para.N)
print 'Exploration temperature: ' + str(self.para.d[i])
print '-----------------------------------------'
stats, qv = self.multiQV(ITER=self.para.iters, type='ASGD', partial=True)
for h in range(2):
V_e[i, h] = qv[h].ve
P_e[i, h] = qv[h].pe
self.users.update_policy(qv[0].W_f, qv[1].W_f, Np=self.para.update_rate[i], N_e=self.para.N_e[i + 1], d=self.para.d[i + 1])
### ================================
# Optimal Inflow shares
if self.para.opt_lam:
counter += 1
if counter > 8 and i > 16:
counter = 0
ITER = self.sim.ITEROLD
SW[j] = self.sim.stats['SW']['Mean'][ITER]
if j == 0:
Lambda[j + 1] = max(min(Lambda[j] + delta*0.5, 0.99), 0.01)
LambdaK[j + 1] = LambdaK[j]
SW_max = SW[j]
else:
if self.para.unbundled:
if Kchange:
if SW[j] > SW_max:
deltaK *= 0.9
LambdaK[j + 1] = LambdaK[j]
SW_max = SW[j]
else:
deltaK *= -0.75
LambdaK[j + 1] = LambdaK[j - 1]
Lambda[j + 1] = max(min(Lambda[j] + 1 * delta, 0.99), 0.01)
Kchange = False
else:
if SW[j] > SW_max:
delta *= 0.9
Lambda[j + 1] = Lambda[j]
SW_max = SW[j]
else:
delta *= -0.75
Lambda[j + 1] = Lambda[j - 1]
LambdaK[j + 1] = max(min(LambdaK[j] + 1 * deltaK, 0.99), 0.01)
Kchange = True
pylab.scatter(Lambda[0:j+1], SW[0:j+1])
pylab.show()
pylab.scatter(LambdaK[0:j+1], SW[0:j+1])
pylab.show()
print '--- Optimal Inflow share search ---'
print 'Lambda previous: ' + str(Lambda[j -1])
print 'Lambda: ' + str(Lambda[j])
print 'Lambda next: ' + str(Lambda[j + 1])
print '--- Optimal Capacity share search ---'
print 'Lambda K previous: ' + str(LambdaK[j -1])
print 'Lambda K: ' + str(LambdaK[j])
print 'Lambda K next: ' + str(LambdaK[j + 1])
print 'Old welfare: ' + str(SW[j-1])
print 'Welfare: ' + str(SW[j])
print 'Welfare: ' + str(SW_max)
else:
if SW[j] > SW[j - 1]:
delta *= 0.9
else:
delta *= -0.75
Lambda[j + 1] = max(min(Lambda[j] + 1 * delta, 0.99), 0.01)
#pylab.scatter(Lambda[0:j+1], SW[0:j+1])
#pylab.show()
print '--- Optimal share search ---'
print 'Lambda previous: ' + str(Lambda[j -1])
print 'Lambda: ' + str(Lambda[j])
print 'Lambda next: ' + str(Lambda[j + 1])
print 'Old welfare: ' + str(SW[j-1])
print 'Welfare: ' + str(SW[j])
self.users.set_shares(Lambda[j + 1], self.para.unbundled, LambdaK[j + 1])
self.utility.set_shares(Lambda[j + 1], self.users)
j += 1
### ===================================
self.users.exploring = 0
self.sim.simulate(self.users, self.storage, self.utility, self.para.T2, self.para.CPU_CORES, stats = True)
self.para.ITER2 = temp
big_toc = time()
print "Total time (minutes): " + str(round((big_toc - big_tic) / 60,2))
Lambda_high = self.utility.Lambda_high
if self.para.unbundled:
Lambda_K = LambdaK[j]
else:
Lambda_K = Lambda_high
self.sim.finalise_stats()
stats = self.sim.stats
self.CSLambda = Lambda_high
print '-------- Final Lambda: ' + str(self.utility.Lambda_high)
return stats, Lambda_high, Lambda_K
def chapter6_extra(self, points):
# Buld grid
Sbar_grid = np.linspace(0, self.para.K, points)
Lambda_grid = np.linspace(0.01, 0.99, points)
self.Lambda, self.Sbar = np.meshgrid(Lambda_grid, Sbar_grid)
#Result arrays
self.SW = np.zeros([points, points])
self.U_low = np.zeros([points, points])
self.U_high = np.zeros([points, points])
self.yield_low = np.zeros([points, points])
self.yield_high = np.zeros([points, points])
self.SD_low = np.zeros([points, points])
self.SD_high = np.zeros([points, points])
self.P_low = np.zeros([points, points])
self.P_high = np.zeros([points, points])
for i in range(points):
for j in range(points):
if (self.Lambda[i,j] * self.storage.K) < self.Sbar[i, j]:
self.SW[i, j], self.U_low[i, j], self.U_high[i, j], self.yield_low[i, j], self.yield_high[i, j], self.SD_low[i, j], self.SD_high[i, j], self.P_low[i, j], self.P_high[i, j] = self.simulate_SOP(100000, self.Sbar[i, j], self.Lambda[i, j])
if np.isnan(self.yield_high[i,j]) or self.yield_high[i, j] == 0:
self.yield_high[i,j] = 10
if np.isnan(self.yield_low[i,j]) or self.yield_low[i, j] == 0:
self.yield_low[i,j] = np.nan
index = self.SW > 10000000000000
self.SW[index] = -10
maxSW = np.max(self.SW)
maxindex = np.where(self.SW == maxSW)
yh = self.yield_high[maxindex]
index1 = (self.yield_high > yh - 0.005)
index2 = (self.yield_high < yh + 0.005)
index3 = index1*index2
for i in range(points):
for j in range(points):
if self.yield_high[i,j] == 10:
self.yield_high[i,j] = np.min(self.yield_high[i, :])
print 'Optimal Lambda: ' + str(self.Lambda[maxindex])
print 'Optimal Sbar: ' + str(self.Sbar[maxindex])
print 'Optimal yield high: ' + str(self.yield_high[maxindex])
from results.chartbuilder import chart_params
chart_params()
home = '/home/nealbob'
out = '/Dropbox/Thesis/IMG/chapter6/'
pylab.figure()
CS = pylab.contour(self.Lambda, self.Sbar, self.yield_high, [0.7, 0.8, 0.9, 0.98])
pylab.clabel(CS)
pylab.xlabel(r'$\Lambda_{high} K$')
pylab.ylabel(r'$\overline{S}$')
pylab.savefig(home + out + 'yield_high_c.pdf', bbox_inches='tight')
pylab.show()
pylab.figure()
CS = pylab.contour(self.Lambda, self.Sbar, self.yield_low, [0.9, 0.7, 0.5, 0.3])
pylab.clabel(CS)
pylab.xlabel(r'$\Lambda_{high} K$')
pylab.ylabel(r'$\overline{S}$')
pylab.savefig(home + out + 'yield_low_c.pdf', bbox_inches='tight')
pylab.show()
pylab.figure()
pylab.scatter(self.Lambda[index3], self.Sbar[index3])
pylab.xlabel(r'$\Lambda_{high} K$')
pylab.savefig(home + out + 'yield_low_s.pdf', bbox_inches='tight')
pylab.show()
pylab.figure()
pylab.plot(self.Lambda[index3], self.yield_low[index3], 'o', label='low')
pylab.plot(self.Lambda[index3], self.yield_high[index3], 'o', label='high')
pylab.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=4, mode="expand", borderaxespad=0.)
pylab.xlabel(r'$\Lambda_{high} K$')
pylab.savefig(home + out + 'yield_s.pdf', bbox_inches='tight')
pylab.show()
pylab.figure()
pylab.plot(self.Lambda[index3], self.P_low[index3], 'o', label='low')
pylab.plot(self.Lambda[index3], self.P_high[index3], 'o', label='high')
pylab.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=4, mode="expand", borderaxespad=0.)
pylab.xlabel(r'$\Lambda_{high} K$')
pylab.savefig(home + out + 'P_high_s.pdf', bbox_inches='tight')
pylab.show()
def chapter5(self):
print '\n --- Scenario --- \n'
print str(self.para.sr) + ' storage rights, loss deductions = ' + str(self.para.ls) + ', priority = ' + str(self.para.HL) + '\n'
if self.para.sr == 'NS':
NS = True
else:
NS = False
big_tic = time()
# Planners problem
p_stats, sdp, p_st = self.plannerSDP()
################## User starting values #################
print 'User starting values, fitted Q-iteration ...'
self.users.set_explorers(self.para.N_e[0], self.para.d[0])
stats, qv = self.multiQV(ITER=self.para.ITER1, init=True, type='ASGD', NS=NS)
################## Main Q-learning #################
V_e = np.zeros([self.para.ITER2, 2]) # Value error
P_e = np.zeros([self.para.ITER2, 2]) # Policy error