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move physbo.search.discrete.policy_mo to physbo.search.discrete_multi.policy #13

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merged 4 commits into from
Mar 3, 2021
Merged

move physbo.search.discrete.policy_mo to physbo.search.discrete_multi.policy #13

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edf8f3e
added e2e test for multi_objective optimization
yomichi Mar 3, 2021
cec643d
move *_mo to a new submodule, physbo.search.discrete_multi
yomichi Mar 3, 2021
6f868d3
added missing directory
yomichi Mar 3, 2021
cb0f5b0
update tutorial for multi-objective optimization
yomichi Mar 3, 2021
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287 changes: 239 additions & 48 deletions docs/sphinx/manual/en/source/notebook/tutorial_multi_objective.ipynb
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36 changes: 18 additions & 18 deletions docs/sphinx/manual/ja/source/notebook/tutorial_multi_objective.ipynb
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1 change: 1 addition & 0 deletions physbo/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,5 +3,6 @@
from . import blm
from . import misc
from . import search
from . import predictor
from .predictor import base_predictor
from .variable import variable
2 changes: 2 additions & 0 deletions physbo/search/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,2 +1,4 @@
from . import score
from . import discrete
from . import score_multi
from . import discrete_multi
7 changes: 3 additions & 4 deletions physbo/search/discrete/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
from .policy import policy, policy_mo
from .results import history, history_mo
from .pareto import Pareto
from .policy import policy
from .results import history

__all__ = ["policy", "policy_mo", "history", "history_mo", "Pareto"]
# __all__ = ["policy", "history"]
298 changes: 11 additions & 287 deletions physbo/search/discrete/policy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,14 +3,13 @@
import pickle as pickle
import itertools

from .results import history, history_mo
from .results import history
from .. import utility
from .. import score as search_score
from ...gp import predictor as gp_predictor
from ...blm import predictor as blm_predictor
from ...misc import set_config

import physbo
import physbo.gp
import physbo.blm
import physbo.misc
import physbo.search.score
from physbo.variable import variable

MAX_SEACH = int(20000)
Expand Down Expand Up @@ -300,11 +299,11 @@ def get_score(self, mode, predictor=None, training=None, alpha=1):

test = self.test.get_subset(actions)
if mode == "EI":
f = physbo.search.score.EI(predictor, training, test)
f = search_score.EI(predictor, training, test)
elif mode == "PI":
f = physbo.search.score.PI(predictor, training, test)
f = search_score.PI(predictor, training, test)
elif mode == "TS":
f = physbo.search.score.TS(predictor, training, test, alpha)
f = search_score.TS(predictor, training, test, alpha)
else:
raise NotImplementedError("mode must be EI, PI or TS.")
return f
Expand Down Expand Up @@ -576,9 +575,9 @@ def _init_predictor(self, is_rand_expans, predictor=None):
self.predictor = self._set_predictor(predictor)
if self.predictor is None:
if is_rand_expans:
self.predictor = physbo.blm.predictor(self.config)
self.predictor = blm_predictor(self.config)
else:
self.predictor = physbo.gp.predictor(self.config)
self.predictor = gp_predictor(self.config)

return self.predictor

Expand Down Expand Up @@ -657,280 +656,5 @@ def _set_config(self, config=None):

"""
if config is None:
config = physbo.misc.set_config()
config = set_config()
return config


class policy_mo(policy):
def __init__(self, test_X, num_objectives, comm=None, config=None, initial_actions=None):
self.num_objectives = num_objectives
self.history = history_mo(num_objectives=self.num_objectives)

self.training_list = [variable() for i in range(self.num_objectives)]
self.predictor_list = [None for i in range(self.num_objectives)]
self.test_list = [self._set_test(test_X) for i in range(self.num_objectives)]
self.new_data_list = [None for i in range(self.num_objectives)]

self.actions = np.arange(0, test_X.shape[0])
self.config = self._set_config(config)

self.TS_candidate_num = None

if initial_actions is not None:
self.actions = sorted(list(set(self.actions) - set(initial_actions)))

if comm is None:
self.mpicomm = None
self.mpisize = 1
self.mpirank = 0
else:
self.mpicomm = comm
self.mpisize = comm.size
self.mpirank = comm.rank
self.actions = np.array_split(self.actions, self.mpisize)[self.mpirank]

def write(self, action, t, X=None):
self.history.write(t, action)
action = np.array(action)
t = np.array(t)

for i in range(self.num_objectives):
test = self.test_list[i]
predictor = self.predictor_list[i]

if X is None:
X = test.X[action, :]
Z = test.Z[action, :] if test.Z is not None else None
else:
Z = predictor.get_basis(X) if predictor is not None else None

self.new_data_list[i] = variable(X, t[:, i], Z)
self.training_list[i].add(X=X, t=t[:, i], Z=Z)

def _switch_model(self, i):
self.training = self.training_list[i]
self.predictor = self.predictor_list[i]
self.test = self.test_list[i]
self.new_data = self.new_data_list[i]

def random_search(
self,
max_num_probes,
num_search_each_probe=1,
simulator=None,
is_disp=True,
disp_pareto_set=False,
):

if self.mpirank != 0:
is_disp = False

N = int(num_search_each_probe)

if int(max_num_probes) * N > len(self.actions):
raise ValueError(
"max_num_probes * num_search_each_probe must \
be smaller than the length of candidates"
)

if is_disp:
utility.show_interactive_mode(simulator, self.history)

for n in range(0, max_num_probes):

if is_disp and N > 1:
utility.show_start_message_multi_search_mo(
self.history.num_runs, "random"
)

action = self.get_random_action(N)

if simulator is None:
return action

t = run_simulator(simulator, action, self.mpicomm)
self.write(action, t)

if is_disp:
utility.show_search_results_mo(
self.history, N, disp_pareto_set=disp_pareto_set
)

return copy.deepcopy(self.history)

def bayes_search(
self,
training_list=None,
max_num_probes=None,
num_search_each_probe=1,
predictor_list=None,
is_disp=True,
disp_pareto_set=False,
simulator=None,
score="HVPI",
interval=0,
num_rand_basis=0,
):

if self.mpirank != 0:
is_disp = False

if max_num_probes is None:
max_num_probes = 1
simulator = None

is_rand_expans = False if num_rand_basis == 0 else True

if training_list is not None:
self.training_list = training_list

if predictor_list is None:
if is_rand_expans:
self.predictor_list = [
physbo.blm.predictor(self.config) for i in range(self.num_objectives)
]
else:
self.predictor_list = [
physbo.gp.predictor(self.config) for i in range(self.num_objectives)
]
else:
self.predictor = predictor_list

N = int(num_search_each_probe)

for n in range(max_num_probes):

if utility.is_learning(n, interval):
for i in range(self.num_objectives):
self._switch_model(i)
self.predictor.fit(self.training, num_rand_basis)
self.test.Z = self.predictor.get_basis(self.test.X)
self.training.Z = self.predictor.get_basis(self.training.X)
self.predictor.prepare(self.training)
else:
for i in range(self.num_objectives):
self._switch_model(i)
self.predictor.update(self.training, self.new_data)

if num_search_each_probe != 1:
utility.show_start_message_multi_search_mo(self.history.num_runs, score)

K = self.config.search.multi_probe_num_sampling
alpha = self.config.search.alpha
action = self.get_actions(score, N, K, alpha)

if simulator is None:
return action

t = run_simulator(simulator, action, self.mpicomm)
self.write(action, t)

if is_disp:
utility.show_search_results_mo(
self.history, N, disp_pareto_set=disp_pareto_set
)

return copy.deepcopy(self.history)

def get_actions(self, mode, N, K, alpha):
f = self.get_score(
mode, self.predictor_list, self.training_list, self.history.pareto, alpha
)
champion, local_champion, local_index = self._find_champion(f)
chosen_actions = np.zeros(N, dtype=int)
chosen_actions[0] = champion
if champion == local_champion:
self.actions = self.delete_actions(local_index)

for n in range(1, N):
f = self.get_score(
mode,
self.predictor_list,
self.training_list,
self.history.pareto,
alpha,
)
champion, local_champion, local_index = self._find_champion(f)
chosen_actions[n] = champion
if champion == local_champion:
self.actions = self.delete_actions(local_index)
return chosen_actions

def get_score(self, mode, predictor_list, training_list, pareto, alpha=1):
actions = self.actions
test = self.test.get_subset(actions)

if mode == "EHVI":
fmean, fstd = self.get_fmean_fstd(predictor_list, training_list, test)
f = physbo.search.score.EHVI(fmean, fstd, pareto)
elif mode == "HVPI":
fmean, fstd = self.get_fmean_fstd(predictor_list, training_list, test)
f = physbo.search.score.HVPI(fmean, fstd, pareto)
elif mode == "TS":
f = physbo.search.score.TS_MO(
predictor_list,
training_list,
test,
alpha,
reduced_candidate_num=self.TS_candidate_num,
)
else:
raise NotImplementedError("mode must be EHVI, HVPI or TS.")
return f

def get_fmean_fstd(self, predictor_list, training_list, test):
fmean = [
predictor.get_post_fmean(training, test)
for predictor, training in zip(predictor_list, training_list)
]
fcov = [
predictor.get_post_fcov(training, test)
for predictor, training in zip(predictor_list, training_list)
]

# shape: (N, n_obj)
fmean = np.array(fmean).T
fstd = np.sqrt(np.array(fcov)).T
return fmean, fstd

def load(self, file_history, file_training_list=None, file_predictor_list=None):
self.history.load(file_history)

if file_training_list is None:
N = self.history.total_num_search
X = self.test_list[0].X[self.history.chosen_actions[0:N], :]
t = self.history.fx[0:N]
self.training_list = [
variable(X=X, t=t[:, i]) for i in range(self.num_objectives)
]
else:
self.load_training_list(file_training_list)

if file_predictor_list is not None:
self.load_predictor_list(file_predictor_list)

def save_predictor_list(self, file_name):
with open(file_name, "wb") as f:
pickle.dump(self.predictor_list, f, 2)

def save_training_list(self, file_name):
obj = [
{"X": training.X, "t": training.t, "Z": training.Z}
for training in self.training_list
]
with open(file_name, "wb") as f:
pickle.dump(obj, f, 2)

def load_predictor_list(self, file_name):
with open(file_name, "rb") as f:
self.predictor_list = pickle.load(f)

def load_training_list(self, file_name):
with open(file_name, "rb") as f:
data_list = pickle.load(f)

self.training_list = [variable() for i in range(self.num_objectives)]
for data, training in zip(data_list, self.training_list):
training.X = data["X"]
training.t = data["t"]
training.Z = data["Z"]
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