import numpy as np
import pickle
from .. import pareto
MAX_SEARCH = int(30000)
[docs]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)
[docs] def write(self, t, action):
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)
[docs] def export_pareto_front(self):
return self.pareto.export_front()
[docs] 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)
[docs] def load(self, filename):
with open(filename, 'rb') as f:
data = pickle.load(f)
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']
self.pareto = data['pareto']