# SPDX-License-Identifier: MPL-2.0
# Copyright (C) 2020- The University of Tokyo
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://mozilla.org/MPL/2.0/.
import numpy as np
import pickle
import copy
from .. import pareto
MAX_SEARCH = int(30000)
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class history(object):
def __init__(self, num_objectives):
self.num_objectives = num_objectives
self.pareto = pareto.Pareto(num_objectives=self.num_objectives)
self.num_runs = int(0)
self.total_num_search = int(0)
self.fx = np.zeros((MAX_SEARCH, self.num_objectives), dtype=float)
self.chosen_actions = np.zeros(MAX_SEARCH, dtype=int)
self.terminal_num_run = np.zeros(MAX_SEARCH, dtype=int)
self._time_total = np.zeros(MAX_SEARCH, dtype=float)
self._time_update_predictor = np.zeros(MAX_SEARCH, dtype=float)
self._time_get_action = np.zeros(MAX_SEARCH, dtype=float)
self._time_run_simulator = np.zeros(MAX_SEARCH, dtype=float)
@property
def time_total(self):
return copy.copy(self._time_total[0 : self.num_runs])
@property
def time_update_predictor(self):
return copy.copy(self._time_update_predictor[0 : self.num_runs])
@property
def time_get_action(self):
return copy.copy(self._time_get_action[0 : self.num_runs])
@property
def time_run_simulator(self):
return copy.copy(self._time_run_simulator[0 : self.num_runs])
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def write(
self,
t,
action,
time_total=None,
time_update_predictor=None,
time_get_action=None,
time_run_simulator=None,
):
t = np.array(t)
action = np.array(action)
if t.ndim == 1:
N = 1
if len(t) != self.num_objectives:
raise ValueError("t does not match the number of objectives")
else:
N = t.shape[0]
if t.shape[1] != self.num_objectives:
raise ValueError("t does not match the number of objectives")
st = self.total_num_search
en = st + N
self.terminal_num_run[self.num_runs] = en
self.fx[st:en] = t
self.chosen_actions[st:en] = action
self.num_runs += 1
self.total_num_search += N
# update Pareto set
self.pareto.update_front(t)
if time_total is None:
time_total = np.zeros(N, dtype=float)
self._time_total[st:en] = time_total
if time_update_predictor is None:
time_update_predictor = np.zeros(N, dtype=float)
self._time_update_predictor[st:en] = time_update_predictor
if time_get_action is None:
time_get_action = np.zeros(N, dtype=float)
self._time_get_action[st:en] = time_get_action
if time_run_simulator is None:
time_run_simulator = np.zeros(N, dtype=float)
self._time_run_simulator[st:en] = time_run_simulator
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def export_pareto_front(self):
return self.pareto.export_front()
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def save(self, filename):
N = self.total_num_search
M = self.num_runs
obj = {
"num_runs": M,
"total_num_search": N,
"fx": self.fx[0:N],
"chosen_actions": self.chosen_actions[0:N],
"terminal_num_run": self.terminal_num_run[0:M],
"pareto": self.pareto,
}
with open(filename, "wb") as f:
pickle.dump(obj, f)
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def load(self, filename):
with open(filename, "rb") as f:
data = pickle.load(f)
M = int(data["num_runs"])
N = int(data["total_num_search"])
self.num_runs = M
self.total_num_search = N
self.fx[0:N] = data["fx"]
self.chosen_actions[0:N] = data["chosen_actions"]
self.terminal_num_run[0:M] = data["terminal_num_run"]
self.pareto = data["pareto"]