# Information sources, in order from expensive to cheap
sim.IS = [
lambda h, c, s: IS0[' '.join([''.join(h), c, s])],
lambda h, c, s: IS1[' '.join([''.join(h), c, s])]
]
sim.costs = [
2.0,
1.0
]
sim.obj_vs_cost_fname = "obj_vs_cost_misokg.dat"
sim.save_extra_files = True
# sim.historical_nsample = 10
########################################
# Override the possible combinations with the reduced list of IS0
sim.combinations = [k[1] + "Pb" + k[0] + "_" + k[2] + "_" + str(IS) for k in [key.split() for key in IS0.keys()] for IS in range(len(sim.IS))]
combos_no_IS = [k[1] + "Pb" + k[0] + "_" + k[2] for k in [key.split() for key in IS0.keys()]]
# Because we do this, we should also generate our own historical sample
sim.historical_nsample = len(combos_no_IS)
choices = combos_no_IS
tmp_data = pal_strings.alphaToNum(
choices,
solvents,
mixed_halides=True,
name_has_IS=False)
data = []
for IS in range(len(sim.IS)):
for i, d in enumerate(tmp_data):
h, c, _, s, _ = pal_strings.parseName(pal_strings.parseNum(d, solvents, mixed_halides=True, num_has_IS=False), name_has_IS=False)
def run(run_index, folder="data_dumps", infosources=0, exact_cost=True):
# Store data for debugging
IS_N5R2 = pickle.load(open("enthalpy_N5_R2_wo_GBL_Ukcal-mol", 'r'))
IS_N3R2 = pickle.load(open("enthalpy_N3_R2_Ukcal-mol", 'r'))
IS_N1R2 = pickle.load(open("enthalpy_N1_R2_Ukcal-mol", 'r'))
IS_N1R3 = pickle.load(open("enthalpy_N1_R3_Ukcal-mol", 'r'))
if infosources == 0:
IS0 = IS_N1R3
if exact_cost:
costs = [6.0]
else:
costs = [10.0]
elif infosources == 1:
IS0 = IS_N3R2
if exact_cost:
costs = [14.0]
else:
costs = [10.0]
elif infosources == 2:
IS0 = IS_N5R2
if exact_cost:
costs = [27.0]
else:
costs = [100.0]
elif infosources == 3:
IS0 = IS_N5R2
if exact_cost:
costs = [27.0]
else:
costs = [100.0]
else:
raise Exception("HOW?")
# Generate the main object
sim = Optimizer()
# Assign simulation properties
sim.hyperparameter_objective = MLE
###################################################################################################
# File names
sim.fname_out = "enthalpy_ei.dat"
sim.fname_historical = "%s/%d_reduced.history" % (folder, run_index)
print "Waiting on %s to be written..." % sim.fname_historical,
while not all([
os.path.exists(sim.fname_historical),
os.path.exists("%s/%d.combos" % (folder, run_index))
]):
time.sleep(30)
print " DONE"
# Information sources, in order from expensive to cheap
sim.IS = [
lambda h, c, s: -1.0 * IS0[' '.join([''.join(h), c, s])],
]
sim.costs = costs
sim.save_extra_files = False
sim.logger_fname = "%s/%d_ei.log" % (folder, run_index)
if os.path.exists(sim.logger_fname):
os.system("rm %s" % sim.logger_fname)
os.system("touch %s" % sim.logger_fname)
sim.historical_nsample = len(
pickle.load(open("%s/%d_reduced.history" % (folder, run_index), 'r')))
sim.combinations = [
c for c in pickle.load(open("%s/%d.combos" % (folder, run_index), 'r'))
if c.endswith("0")
]
########################################
sim.n_start = 10 # The number of starting MLE samples
# sim.reopt = 20
sim.reopt = float("inf") # Don't reopt hyperparams
sim.ramp_opt = None
sim.parallel = False
# Possible compositions by default
sim.A = ["Cs", "MA", "FA"]
sim.B = ["Pb"]
sim.X = ["Cl", "Br", "I"]
sim.solvents = copy.deepcopy(solvents)
sim.S = list(set([v["name"] for k, v in sim.solvents.items()]))
sim.mixed_halides = True
sim.mixed_solvents = False
# Parameters for debugging and overwritting
sim.debug = False
sim.verbose = True
sim.overwrite = True # If True, warning, else Error
# Functional forms of our mean and covariance
# MEAN: 4 * mu_alpha + mu_zeta
# COV: sig_alpha * |X><X| + sig_beta * I_N + sig_zeta + MaternKernel(S, weights, sig_m)
SCALE = [2.0, 4.0][int(sim.mixed_halides)]
# _1, _2, _3 used as dummy entries
sim.mean = lambda _1, Y, theta: np.array(
[SCALE * theta.mu_alpha + theta.mu_zeta for _ in Y])
def cov(X, Y, theta):
A = theta.sig_alpha * np.dot(
np.array(X)[:, 1:-3],
np.array(X)[:, 1:-3].T)
B = theta.sig_beta * np.diag(np.ones(len(X)))
C = theta.sig_zeta
D = mk52(np.array(X)[:, -3:-1], [theta.l1, theta.l2], theta.sig_m)
return A + B + C + D
sim.cov = cov
sim.theta.bounds = {}
sim.theta.mu_alpha, sim.theta.bounds['mu_alpha'] = None, (
1E-3, lambda _, Y: max(Y))
sim.theta.sig_alpha, sim.theta.bounds['sig_alpha'] = None, (
1E-2, lambda _, Y: 10.0 * np.var(Y))
sim.theta.sig_beta, sim.theta.bounds['sig_beta'] = None, (
1E-2, lambda _, Y: 10.0 * np.var(Y))
sim.theta.mu_zeta, sim.theta.bounds['mu_zeta'] = None, (
1E-3, lambda _, Y: max(Y))
sim.theta.sig_zeta, sim.theta.bounds['sig_zeta'] = None, (
1E-2, lambda _, Y: 10.0 * np.var(Y))
sim.theta.sig_m, sim.theta.bounds['sig_m'] = None, (1E-2,
lambda _, Y: np.var(Y))
sim.theta.l1, sim.theta.bounds['l1'] = None, (1E-1, 1)
sim.theta.l2, sim.theta.bounds['l2'] = None, (1E-1, 1)
# NOTE! This is a reserved keyword in misoKG. We will generate a list of the same length
# of the information sources, and use this for scaling our IS.
# sim.theta.rho, sim.theta.bounds['rho'] = {"[0, 0]": 1}, (1E-1, 5.0)
# NOTE! This is a reserved keyword in misoKG. We will generate a list of the same length
# of the information sources, and use this for scaling our IS.
sim.theta.rho = {"[0, 0]": 1}
sim.theta.bounds['rho [0, 0]'] = (1, 1)
sim.theta.set_hp_names()
h, c, s = min([(IS0[k], k) for k in IS0.keys()])[1].split()
sim.recommendation_kill_switch = "%sPb%s_%s_0" % (c, h, s)
sim.primary_rho_opt = False
sim.update_hp_only_with_IS0 = False
###################################################################################################
# Start simulation
sim.run()