# 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 copy
import pickle
from .. import utility
MAX_SEARCH = int(30000)
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class history:
def __init__(self):
self.num_runs = int(0)
self.total_num_search = int(0)
self.fx = np.zeros(MAX_SEARCH, 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,
):
"""
Overwrite fx and chosen_actions by t and action.
Parameters
----------
t: numpy.ndarray
N dimensional array. The negative energy of each search candidate (value of the objective function to be optimized).
action: numpy.ndarray
N dimensional array. The indexes of actions of each search candidate.
time_total: numpy.ndarray
N dimenstional array. The total elapsed time in each step.
If None (default), filled by 0.0.
time_update_predictor: numpy.ndarray
N dimenstional array. The elapsed time for updating predictor (e.g., learning hyperparemters) in each step.
If None (default), filled by 0.0.
time_get_action: numpy.ndarray
N dimenstional array. The elapsed time for getting next action in each step.
If None (default), filled by 0.0.
time_run_simulator: numpy.ndarray
N dimenstional array. The elapsed time for running the simulator in each step.
If None (default), filled by 0.0.
Returns
-------
"""
N = utility.length_vector(t)
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
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_sequence_best_fx(self):
"""
Export fx and actions at each sequence.
(The total number of data is num_runs.)
Returns
-------
best_fx: numpy.ndarray
best_actions: numpy.ndarray
"""
best_fx = np.zeros(self.num_runs, dtype=float)
best_actions = np.zeros(self.num_runs, dtype=int)
for n in range(self.num_runs):
index = np.argmax(self.fx[0 : self.terminal_num_run[n]])
best_actions[n] = self.chosen_actions[index]
best_fx[n] = self.fx[index]
return best_fx, best_actions
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def export_all_sequence_best_fx(self):
"""
Export all fx and actions at each sequence.
(The total number of data is total_num_research.)
Returns
-------
best_fx: numpy.ndarray
best_actions: numpy.ndarray
"""
best_fx = np.zeros(self.total_num_search, dtype=float)
best_actions = np.zeros(self.total_num_search, dtype=int)
best_fx[0] = self.fx[0]
best_actions[0] = self.chosen_actions[0]
for n in range(1, self.total_num_search):
if best_fx[n - 1] < self.fx[n]:
best_fx[n] = self.fx[n]
best_actions[n] = self.chosen_actions[n]
else:
best_fx[n] = best_fx[n - 1]
best_actions[n] = best_actions[n - 1]
return best_fx, best_actions
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def save(self, filename):
"""
Save the information of the history.
Parameters
----------
filename: str
The name of the file which stores the information of the history
Returns
-------
"""
N = self.total_num_search
M = self.num_runs
np.savez_compressed(
filename,
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],
)
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def load(self, filename):
"""
Load the information of the history.
Parameters
----------
filename: str
The name of the file which stores the information of the history
Returns
-------
"""
data = np.load(filename)
M = data["num_runs"]
N = 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"]