|
1
|
|
|
# Author: Simon Blanke |
|
2
|
|
|
# Email: [email protected] |
|
3
|
|
|
# License: MIT License |
|
4
|
|
|
|
|
5
|
|
|
import numpy as np |
|
6
|
|
|
|
|
7
|
|
|
from ..search_space import SearchSpace |
|
8
|
|
|
from ..model import Model |
|
9
|
|
|
from ..init_position import InitSearchPosition |
|
10
|
|
|
from ..extentions import ShortTermMemory, LongTermMemory |
|
11
|
|
|
|
|
12
|
|
|
|
|
13
|
|
|
class Candidate: |
|
14
|
|
|
def __init__(self, nth_process, _main_args_): |
|
15
|
|
|
self.i = 0 |
|
16
|
|
|
self.memory = _main_args_.memory |
|
17
|
|
|
|
|
18
|
|
|
self._score_best = -np.inf |
|
19
|
|
|
self.pos_best = None |
|
20
|
|
|
self.model = None |
|
21
|
|
|
|
|
22
|
|
|
self.nth_process = nth_process |
|
23
|
|
|
model_nr = nth_process % _main_args_.n_models |
|
24
|
|
|
self.func_ = list(_main_args_.search_config.keys())[model_nr] |
|
25
|
|
|
self._space_ = SearchSpace(_main_args_, model_nr) |
|
26
|
|
|
self.func_name = str(self.func_).split(" ")[1] |
|
27
|
|
|
self._model_ = Model(self.func_, nth_process, _main_args_) |
|
28
|
|
|
self._init_ = InitSearchPosition(self._space_, self._model_, _main_args_) |
|
29
|
|
|
|
|
30
|
|
|
self.eval_time_sum = 0 |
|
31
|
|
|
|
|
32
|
|
|
if not self.memory: |
|
33
|
|
|
self.mem = None |
|
34
|
|
|
self.eval_pos = self.eval_pos_noMem |
|
35
|
|
|
|
|
36
|
|
|
elif self.memory == "short": |
|
37
|
|
|
self.mem = ShortTermMemory(self._space_, _main_args_) |
|
38
|
|
|
self.eval_pos = self.eval_pos_Mem |
|
39
|
|
|
|
|
40
|
|
|
elif self.memory == "long": |
|
41
|
|
|
self.mem = LongTermMemory(self._space_, _main_args_) |
|
42
|
|
|
self.eval_pos = self.eval_pos_Mem |
|
43
|
|
|
|
|
44
|
|
|
self.mem.load_memory(self.func_) |
|
45
|
|
|
|
|
46
|
|
|
else: |
|
47
|
|
|
print("Warning: Memory not defined") |
|
48
|
|
|
self.mem = None |
|
49
|
|
|
self.eval_pos = self.eval_pos_noMem |
|
50
|
|
|
|
|
51
|
|
|
if self.mem: |
|
52
|
|
|
if self.mem.meta_data_found: |
|
53
|
|
|
self.pos_best = self.mem.pos_best |
|
54
|
|
|
self.score_best = self.mem.score_best |
|
55
|
|
|
else: |
|
56
|
|
|
self.pos_best = self._init_._set_start_pos() |
|
57
|
|
|
self.score_best = self.eval_pos(self.pos_best) |
|
58
|
|
|
|
|
59
|
|
|
else: |
|
60
|
|
|
self.pos_best = self._init_._set_start_pos() |
|
61
|
|
|
self.score_best = self.eval_pos(self.pos_best) |
|
62
|
|
|
|
|
63
|
|
|
def _get_warm_start(self): |
|
64
|
|
|
return self._space_.pos2para(self.pos_best) |
|
65
|
|
|
|
|
66
|
|
|
@property |
|
67
|
|
|
def score_best(self): |
|
68
|
|
|
return self._score_best |
|
69
|
|
|
|
|
70
|
|
|
@score_best.setter |
|
71
|
|
|
def score_best(self, value): |
|
72
|
|
|
self.model_best = self.model |
|
73
|
|
|
self._score_best = value |
|
74
|
|
|
|
|
75
|
|
|
def eval_pos_noMem(self, pos): |
|
76
|
|
|
para = self._space_.pos2para(pos) |
|
77
|
|
|
para["iteration"] = self.i |
|
78
|
|
|
score, eval_time, self.model = self._model_.train_model(para) |
|
79
|
|
|
self.eval_time_sum = self.eval_time_sum + eval_time |
|
80
|
|
|
|
|
81
|
|
|
return score |
|
82
|
|
|
|
|
83
|
|
|
def eval_pos_Mem(self, pos, force_eval=False): |
|
84
|
|
|
pos_str = pos.tostring() |
|
85
|
|
|
|
|
86
|
|
|
if pos_str in self.mem.memory_dict and not force_eval: |
|
87
|
|
|
return self.mem.memory_dict[pos_str] |
|
88
|
|
|
else: |
|
89
|
|
|
para = self._space_.pos2para(pos) |
|
90
|
|
|
para["iteration"] = self.i |
|
91
|
|
|
score, eval_time, self.model = self._model_.train_model(para) |
|
92
|
|
|
self.mem.memory_dict[pos_str] = score |
|
93
|
|
|
self.eval_time_sum = self.eval_time_sum + eval_time |
|
94
|
|
|
|
|
95
|
|
|
return score |
|
96
|
|
|
|
SimonBlanke /
Hyperactive
