1
|
|
|
# Author: Simon Blanke |
2
|
|
|
# Email: [email protected] |
3
|
|
|
# License: MIT License |
4
|
|
|
|
5
|
|
|
|
6
|
|
|
import multiprocessing |
7
|
|
|
from tqdm import tqdm |
8
|
|
|
|
9
|
|
|
from .optimizers import RandomSearchOptimizer |
10
|
|
|
from .run_search import run_search |
11
|
|
|
|
12
|
|
|
|
13
|
|
|
def set_n_jobs(n_jobs): |
14
|
|
|
"""Sets the number of jobs to run in parallel""" |
15
|
|
|
num_cores = multiprocessing.cpu_count() |
16
|
|
|
if n_jobs == -1 or n_jobs > num_cores: |
17
|
|
|
return num_cores |
18
|
|
|
else: |
19
|
|
|
return n_jobs |
20
|
|
|
|
21
|
|
|
|
22
|
|
|
class HyperactiveResults: |
23
|
|
|
def __init__(*args, **kwargs): |
24
|
|
|
pass |
25
|
|
|
|
26
|
|
|
def _sort_results_objFunc(self, objective_function): |
27
|
|
|
import numpy as np |
28
|
|
|
import pandas as pd |
29
|
|
|
|
30
|
|
|
best_score = -np.inf |
31
|
|
|
best_para = None |
32
|
|
|
results_list = [] |
33
|
|
|
|
34
|
|
|
for results in self.results_list: |
35
|
|
|
nth_process = results["nth_process"] |
36
|
|
|
|
37
|
|
|
process_infos = self.process_infos[nth_process] |
38
|
|
|
objective_function_ = process_infos["objective_function"] |
39
|
|
|
|
40
|
|
|
if objective_function_ != objective_function: |
41
|
|
|
continue |
42
|
|
|
|
43
|
|
|
if results["best_score"] > best_score: |
44
|
|
|
best_score = results["best_score"] |
45
|
|
|
best_para = results["best_para"] |
46
|
|
|
|
47
|
|
|
results_list.append(results["results"]) |
48
|
|
|
|
49
|
|
|
results = pd.concat(results_list) |
50
|
|
|
|
51
|
|
|
self.objFunc2results[objective_function] = { |
52
|
|
|
"best_para": best_para, |
53
|
|
|
"best_score": best_score, |
54
|
|
|
"results": results, |
55
|
|
|
} |
56
|
|
|
|
57
|
|
|
def _sort_results_search_id(self, search_id): |
58
|
|
|
for results in self.results_list: |
59
|
|
|
nth_process = results["nth_process"] |
60
|
|
|
search_id_ = self.process_infos[nth_process]["search_id"] |
61
|
|
|
|
62
|
|
|
if search_id_ != search_id: |
63
|
|
|
continue |
64
|
|
|
|
65
|
|
|
best_score = results["best_score"] |
66
|
|
|
best_para = results["best_para"] |
67
|
|
|
results = results["results"] |
68
|
|
|
|
69
|
|
|
self.search_id2results[search_id] = { |
70
|
|
|
"best_para": best_para, |
71
|
|
|
"best_score": best_score, |
72
|
|
|
"results": results, |
73
|
|
|
} |
74
|
|
|
|
75
|
|
|
def _get_one_result(self, id_, result_name): |
76
|
|
|
if isinstance(id_, str): |
77
|
|
|
if id_ not in self.search_id2results: |
78
|
|
|
self._sort_results_search_id(id_) |
79
|
|
|
|
80
|
|
|
return self.search_id2results[id_][result_name] |
81
|
|
|
|
82
|
|
|
else: |
83
|
|
|
if id_ not in self.objFunc2results: |
84
|
|
|
self._sort_results_objFunc(id_) |
85
|
|
|
|
86
|
|
|
return self.objFunc2results[id_][result_name] |
87
|
|
|
|
88
|
|
|
def best_para(self, id_): |
89
|
|
|
return self._get_one_result(id_, "best_para") |
90
|
|
|
|
91
|
|
|
def best_score(self, id_): |
92
|
|
|
return self._get_one_result(id_, "best_score") |
93
|
|
|
|
94
|
|
|
def results(self, id_): |
95
|
|
|
return self._get_one_result(id_, "results") |
96
|
|
|
|
97
|
|
|
|
98
|
|
|
class Hyperactive(HyperactiveResults): |
99
|
|
|
def __init__( |
100
|
|
|
self, |
101
|
|
|
verbosity=["progress_bar", "print_results", "print_times"], |
102
|
|
|
distribution={ |
103
|
|
|
"multiprocessing": { |
104
|
|
|
"initializer": tqdm.set_lock, |
105
|
|
|
"initargs": (tqdm.get_lock(),), |
106
|
|
|
} |
107
|
|
|
}, |
108
|
|
|
): |
109
|
|
|
super().__init__() |
110
|
|
|
if verbosity is False: |
111
|
|
|
verbosity = [] |
112
|
|
|
|
113
|
|
|
self.verbosity = verbosity |
114
|
|
|
self.distribution = distribution |
115
|
|
|
self.search_ids = [] |
116
|
|
|
|
117
|
|
|
self.process_infos = {} |
118
|
|
|
|
119
|
|
|
self.objFunc2results = {} |
120
|
|
|
self.search_id2results = {} |
121
|
|
|
|
122
|
|
|
def _add_search_processes( |
123
|
|
|
self, |
124
|
|
|
random_state, |
125
|
|
|
objective_function, |
126
|
|
|
search_space, |
127
|
|
|
optimizer, |
128
|
|
|
n_iter, |
129
|
|
|
n_jobs, |
130
|
|
|
max_score, |
131
|
|
|
memory, |
132
|
|
|
memory_warm_start, |
133
|
|
|
search_id, |
134
|
|
|
): |
135
|
|
|
for nth_job in range(set_n_jobs(n_jobs)): |
136
|
|
|
nth_process = len(self.process_infos) |
137
|
|
|
|
138
|
|
|
self.process_infos[nth_process] = { |
139
|
|
|
"random_state": random_state, |
140
|
|
|
"verbosity": self.verbosity, |
141
|
|
|
"nth_process": nth_process, |
142
|
|
|
"objective_function": objective_function, |
143
|
|
|
"search_space": search_space, |
144
|
|
|
"optimizer": optimizer, |
145
|
|
|
"n_iter": n_iter, |
146
|
|
|
"max_score": max_score, |
147
|
|
|
"memory": memory, |
148
|
|
|
"memory_warm_start": memory_warm_start, |
149
|
|
|
"search_id": search_id, |
150
|
|
|
} |
151
|
|
|
|
152
|
|
|
def add_search( |
153
|
|
|
self, |
154
|
|
|
objective_function, |
155
|
|
|
search_space, |
156
|
|
|
n_iter, |
157
|
|
|
search_id=None, |
158
|
|
|
optimizer="default", |
159
|
|
|
n_jobs=1, |
160
|
|
|
initialize={"grid": 4, "random": 2, "vertices": 4}, |
161
|
|
|
max_score=None, |
162
|
|
|
random_state=None, |
163
|
|
|
memory=True, |
164
|
|
|
memory_warm_start=None, |
165
|
|
|
): |
166
|
|
|
|
167
|
|
|
if isinstance(optimizer, str): |
168
|
|
|
if optimizer == "default": |
169
|
|
|
optimizer = RandomSearchOptimizer() |
170
|
|
|
optimizer.init(search_space, initialize) |
171
|
|
|
|
172
|
|
|
if search_id is not None: |
173
|
|
|
search_id = search_id |
174
|
|
|
self.search_ids.append(search_id) |
175
|
|
|
else: |
176
|
|
|
search_id = str(len(self.search_ids)) |
177
|
|
|
self.search_ids.append(search_id) |
178
|
|
|
|
179
|
|
|
self._add_search_processes( |
180
|
|
|
random_state, |
181
|
|
|
objective_function, |
182
|
|
|
search_space, |
183
|
|
|
optimizer, |
184
|
|
|
n_iter, |
185
|
|
|
n_jobs, |
186
|
|
|
max_score, |
187
|
|
|
memory, |
188
|
|
|
memory_warm_start, |
189
|
|
|
search_id, |
190
|
|
|
) |
191
|
|
|
|
192
|
|
|
def run( |
193
|
|
|
self, |
194
|
|
|
max_time=None, |
195
|
|
|
): |
196
|
|
|
for nth_process in self.process_infos.keys(): |
197
|
|
|
self.process_infos[nth_process]["max_time"] = max_time |
198
|
|
|
|
199
|
|
|
self.results_list = run_search(self.process_infos, self.distribution) |
200
|
|
|
|