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compute_simulations.py
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compute_simulations.py
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######## Solve
import pandas
import numpy
from collections import OrderedDict
from calibrations import *
from time_consistent import solve as solve_time_consistent
from time_consistent import simulate
import bttt
from bttt.trees import DeterministicTree, get_ts
from bttt.trees import DeathTree
from bttt.model import import_tree_model
N = 25
T = 25
model_types =['optimal','peg','volume','time-consistent']
results = OrderedDict()
for c,v in list_of_calibrations.items():
calib = v.copy()
beta = calib['beta']
a = calib['a']
p = calib['p']
zbar = calib['zbar']
max_R=5
if c[1]=='time-consistent':
if beta<0.82:
max_R=4
else:
max_R=4
r = calib['Rbar']
# sol = solve_time_consistent(max_R=2, **v)
sol = solve_time_consistent(max_R=max_R, order=100, **v)
sim_tc = simulate(r, sol, T)
results[c] = sim_tc
else:
print(p,c[1])
# if p == 1:
tree = DeterministicTree(N)
for s in tree.nodes:
tree.values[s] = zbar
model = import_tree_model('models.yaml', key=c[1], tree=tree)
model.calibration.update(calib)
sol = model.solve(verbose=True)
df = numpy.concatenate( [get_ts(tree, sol, varname)[:,None] for varname in ['e','f', 'Gamma']], axis=1 )
df = pandas.DataFrame(df, columns=['e','f','Gamma'])
results[c] = df
def Gamma(t, e, parm):
tot = 0
estar = parm['estar']
beta = parm['beta']
p = parm['p']
alpha = parm['a']/(parm['a']+parm['c'])
for s in range(t+1):
tot+=(beta)**s*alpha**(t-s)*(e[s]-estar)
return tot
list_of_calibrations
for c,sim in results.items():
print(c)
parm = list_of_calibrations[c]
# add level of reserves
Rbar = c[2]
p = parm['p']
sim['R'] = Rbar - sim['f'].cumsum().shift()
sim['R'][0] = Rbar # - sim['f'].cumsum().shift()
# add Gamma
gg = [p**t*Gamma(t, sim['e'], parm) for t in range(T)]
if 'Gamma' in sim.columns:
diff = abs(sim['Gamma'] - gg).max()
# print(diff)
# if diff>1e-6:
# raise Exception("Incorrect computation of Gamma")
else:
sim['Gamma'] = gg
#### save results
from dolo import groot
groot()
import pickle
with open("precomputed_simulations.pickle", 'wb') as f:
pickle.dump({'results': results, 'calibrations': list_of_calibrations}, f)
#################333
####################
decision_rules = OrderedDict()
for p in [1.0, 0.9, 0.8]:
v = list_of_calibrations[('baseline','optimal',1.0)].copy()
v['p'] = p
sol = solve_time_consistent(max_R=7, order=1000, verbose=True,**v)
sol = sol[:3]
decision_rules[p] = sol
import pickle
with open("precomputed_decision_rules.pickle",'wb') as f:
pickle.dump({'decision_rules': decision_rules, 'calibration': v}, f)
#################
#################
from bttt.model import import_tree_model
from collections import OrderedDict
model = import_tree_model('models.yaml', key='moving_target', tree=tree)
v = list_of_calibrations[('baseline', 'optimal', 1.0)].copy()
od = OrderedDict()
for R in [0.01, 1]:
lamvec = [0.8, 0.85, 0.9, 0.95, 1.0]
for lam in lamvec:
vv = v.copy()
vv['lam'] = lam
vv['Rbar'] = R
model.calibration.update(vv)
sol = model.solve()
df = numpy.concatenate( [get_ts(tree, sol, varname)[:,None] for varname in ['e','f', 'Gamma', 'target']], axis=1 )
df = pandas.DataFrame(df, columns=['e','f','Gamma','target'])
od[(R,lam)] = df
import pickle
with open("precomputed_moving_target.pickle",'wb') as f:
pickle.dump({'simulations': od, 'calibration': v}, f)