-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathinterruptible-optimization.html
488 lines (446 loc) · 35.9 KB
/
interruptible-optimization.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta charset="utf-8">
<meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="Description" content="scikit-optimize: machine learning in Python">
<title>Interruptible optimization runs with checkpoints — scikit-optimize 0.9.0 documentation</title>
<link rel="canonical" href="https://scikit-optimize.github.io/auto_examples/interruptible-optimization.html" />
<link rel="shortcut icon" href="../_static/favicon.ico"/>
<link rel="stylesheet" href="../_static/css/vendor/bootstrap.min.css" type="text/css" />
<link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
<link rel="stylesheet" href="../_static/sg_gallery.css" type="text/css" />
<link rel="stylesheet" href="../_static/sg_gallery-binder.css" type="text/css" />
<link rel="stylesheet" href="../_static/sg_gallery-dataframe.css" type="text/css" />
<link rel="stylesheet" href="../_static/sg_gallery-rendered-html.css" type="text/css" />
<link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
<script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
<script src="../_static/jquery.js"></script>
</head>
<body>
<nav id="navbar" class="sk-docs-navbar navbar navbar-expand-md navbar-light bg-light py-0">
<div class="container-fluid sk-docs-container px-0">
<a class="navbar-brand py-0" href="../index.html">
<img
class="sk-brand-img"
src="../_static/logo.png"
alt="logo"/>
</a>
<button
id="sk-navbar-toggler"
class="navbar-toggler"
type="button"
data-toggle="collapse"
data-target="#navbarSupportedContent"
aria-controls="navbarSupportedContent"
aria-expanded="false"
aria-label="Toggle navigation"
>
<span class="navbar-toggler-icon"></span>
</button>
<div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
<ul class="navbar-nav mr-auto">
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../install.html">Install</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../user_guide.html">User Guide</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../modules/classes.html">API</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" href="index.html">Examples</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../getting_started.html">Getting Started</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../development.html">Development</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://github.com/scikit-optimize/scikit-optimize">GitHub</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://scikit-optimize.github.io/dev/versions.html">Other Versions</a>
</li>
<li class="nav-item dropdown nav-more-item-dropdown">
<a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
<div class="dropdown-menu" aria-labelledby="navbarDropdown">
<a class="sk-nav-dropdown-item dropdown-item" href="../getting_started.html">Getting Started</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../development.html">Development</a>
<a class="sk-nav-dropdown-item dropdown-item" href="https://github.com/scikit-optimize/scikit-optimize">GitHub</a>
<a class="sk-nav-dropdown-item dropdown-item" href="https://scikit-optimize.github.io/dev/versions.html">Other Versions</a>
</div>
</li>
</ul>
<div id="searchbox" role="search">
<div class="searchformwrapper">
<form class="search" action="../search.html" method="get">
<input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
<input class="sk-search-text-btn" type="submit" value="Go" />
</form>
</div>
</div>
</div>
</div>
</nav>
<div class="d-flex" id="sk-doc-wrapper">
<input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
<label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
<div id="sk-sidebar-wrapper" class="border-right">
<div class="sk-sidebar-toc-wrapper">
<div class="sk-sidebar-toc-logo">
<a href="../index.html">
<img
class="sk-brand-img"
src="../_static/logo.png"
alt="logo"/>
</a>
</div>
<div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
<a href="store-and-load-results.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Store and load skopt optimization results">Prev</a><a href="index.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Examples">Up</a>
<a href="hyperparameter-optimization.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Tuning a scikit-learn estimator with skopt">Next</a>
</div>
<div class="alert alert-danger p-1 mb-2" role="alert">
<p class="text-center mb-0">
<strong>scikit-optimize 0.9.0</strong><br/>
<a href="https://scikit-optimize.github.io/dev/versions.html">Other versions</a>
</p>
</div>
<div class="sk-sidebar-toc">
<ul>
<li><a class="reference internal" href="#">Interruptible optimization runs with checkpoints</a><ul>
<li><a class="reference internal" href="#problem-statement">Problem statement</a></li>
<li><a class="reference internal" href="#simple-example">Simple example</a></li>
<li><a class="reference internal" href="#restoring-the-last-checkpoint">Restoring the last checkpoint</a></li>
<li><a class="reference internal" href="#continue-the-search">Continue the search</a></li>
<li><a class="reference internal" href="#possible-problems">Possible problems</a></li>
</ul>
</li>
</ul>
</div>
</div>
</div>
<div id="sk-page-content-wrapper">
<div class="sk-page-content container-fluid body px-md-3" role="main">
<div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-interruptible-optimization-py"><span class="std std-ref">here</span></a>
to download the full example code or to run this example in your browser via Binder</p>
</div>
<section class="sphx-glr-example-title" id="interruptible-optimization-runs-with-checkpoints">
<span id="sphx-glr-auto-examples-interruptible-optimization-py"></span><h1>Interruptible optimization runs with checkpoints<a class="headerlink" href="#interruptible-optimization-runs-with-checkpoints" title="Permalink to this headline">¶</a></h1>
<p>Christian Schell, Mai 2018
Reformatted by Holger Nahrstaedt 2020</p>
<section id="problem-statement">
<h2>Problem statement<a class="headerlink" href="#problem-statement" title="Permalink to this headline">¶</a></h2>
<p>Optimization runs can take a very long time and even run for multiple days.
If for some reason the process has to be interrupted results are irreversibly
lost, and the routine has to start over from the beginning.</p>
<p>With the help of the <a class="reference internal" href="../modules/generated/skopt.callbacks.CheckpointSaver.html#skopt.callbacks.CheckpointSaver" title="skopt.callbacks.CheckpointSaver"><code class="xref py py-class docutils literal notranslate"><span class="pre">callbacks.CheckpointSaver</span></code></a> callback the optimizer’s current state
can be saved after each iteration, allowing to restart from that point at any
time.</p>
<p>This is useful, for example,</p>
<ul class="simple">
<li><p>if you don’t know how long the process will take and cannot hog computational resources forever</p></li>
<li><p>if there might be system failures due to shaky infrastructure (or colleagues…)</p></li>
<li><p>if you want to adjust some parameters and continue with the already obtained results</p></li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html#numpy.random.seed" title="numpy.random.seed" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span></a><span class="p">(</span><span class="mi">777</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">os</span>
</pre></div>
</div>
</section>
<section id="simple-example">
<h2>Simple example<a class="headerlink" href="#simple-example" title="Permalink to this headline">¶</a></h2>
<p>We will use pretty much the same optimization problem as in the
<a class="reference internal" href="bayesian-optimization.html#sphx-glr-auto-examples-bayesian-optimization-py"><span class="std std-ref">Bayesian optimization with skopt</span></a>
notebook. Additionally we will instantiate the <a class="reference internal" href="../modules/generated/skopt.callbacks.CheckpointSaver.html#skopt.callbacks.CheckpointSaver" title="skopt.callbacks.CheckpointSaver"><code class="xref py py-class docutils literal notranslate"><span class="pre">callbacks.CheckpointSaver</span></code></a>
and pass it to the minimizer:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skopt</span> <span class="kn">import</span> <a href="../modules/generated/skopt.gp_minimize.html#skopt.gp_minimize" title="skopt.gp_minimize" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">gp_minimize</span></a>
<span class="kn">from</span> <span class="nn">skopt</span> <span class="kn">import</span> <span class="n">callbacks</span>
<span class="kn">from</span> <span class="nn">skopt.callbacks</span> <span class="kn">import</span> <a href="../modules/generated/skopt.callbacks.CheckpointSaver.html#skopt.callbacks.CheckpointSaver" title="skopt.callbacks.CheckpointSaver" class="sphx-glr-backref-module-skopt-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">CheckpointSaver</span></a>
<span class="n">noise_level</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="k">def</span> <span class="nf">obj_fun</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">noise_level</span><span class="o">=</span><span class="n">noise_level</span><span class="p">):</span>
<span class="k">return</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.sin.html#numpy.sin" title="numpy.sin" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="mi">5</span> <span class="o">*</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.tanh.html#numpy.tanh" title="numpy.tanh" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">tanh</span></a><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span> <span class="o">+</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randn.html#numpy.random.randn" title="numpy.random.randn" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span></a><span class="p">()</span> \
<span class="o">*</span> <span class="n">noise_level</span>
<span class="n">checkpoint_saver</span> <span class="o">=</span> <a href="../modules/generated/skopt.callbacks.CheckpointSaver.html#skopt.callbacks.CheckpointSaver" title="skopt.callbacks.CheckpointSaver" class="sphx-glr-backref-module-skopt-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">CheckpointSaver</span></a><span class="p">(</span><span class="s2">"./checkpoint.pkl"</span><span class="p">,</span> <span class="n">compress</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span> <span class="c1"># keyword arguments will be passed to `skopt.dump`</span>
<a href="../modules/generated/skopt.gp_minimize.html#skopt.gp_minimize" title="skopt.gp_minimize" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">gp_minimize</span></a><span class="p">(</span><span class="n">obj_fun</span><span class="p">,</span> <span class="c1"># the function to minimize</span>
<span class="p">[(</span><span class="o">-</span><span class="mf">20.0</span><span class="p">,</span> <span class="mf">20.0</span><span class="p">)],</span> <span class="c1"># the bounds on each dimension of x</span>
<span class="n">x0</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mf">20.</span><span class="p">],</span> <span class="c1"># the starting point</span>
<span class="n">acq_func</span><span class="o">=</span><span class="s2">"LCB"</span><span class="p">,</span> <span class="c1"># the acquisition function (optional)</span>
<span class="n">n_calls</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="c1"># number of evaluations of f including at x0</span>
<span class="n">n_random_starts</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="c1"># the number of random initial points</span>
<span class="n">callback</span><span class="o">=</span><span class="p">[</span><span class="n">checkpoint_saver</span><span class="p">],</span>
<span class="c1"># a list of callbacks including the checkpoint saver</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">777</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span> fun: -0.17524445239614728
func_vals: array([-0.04682088, -0.08228249, -0.00653801, -0.07133619, 0.09063509,
0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491])
models: [GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735)]
random_state: RandomState(MT19937) at 0x7F900BD12B40
space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
specs: {'args': {'func': <function obj_fun at 0x7f900af80280>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), 'n_calls': 10, 'n_random_starts': 3, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7F900BD12B40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f900b0e0ee0>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
x: [-18.660711622818603]
x_iters: [[-20.0], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [10.524218484749973], [-17.111120867646513], [7.251301450238415], [-19.16709880491886], [-18.660711622818603], [-18.284297243496926]]
</pre></div>
</div>
<p>Now let’s assume this did not finish at once but took some long time: you
started this on Friday night, went out for the weekend and now, Monday
morning, you’re eager to see the results. However, instead of the
notebook server you only see a blank page and your colleague Garry
tells you that he had had an update scheduled for Sunday noon – who
doesn’t like updates?</p>
<p><a class="reference internal" href="../modules/generated/skopt.gp_minimize.html#skopt.gp_minimize" title="skopt.gp_minimize"><code class="xref py py-class docutils literal notranslate"><span class="pre">gp_minimize</span></code></a> did not finish, and there is no <code class="docutils literal notranslate"><span class="pre">res</span></code> variable with the
actual results!</p>
</section>
<section id="restoring-the-last-checkpoint">
<h2>Restoring the last checkpoint<a class="headerlink" href="#restoring-the-last-checkpoint" title="Permalink to this headline">¶</a></h2>
<p>Luckily we employed the <a class="reference internal" href="../modules/generated/skopt.callbacks.CheckpointSaver.html#skopt.callbacks.CheckpointSaver" title="skopt.callbacks.CheckpointSaver"><code class="xref py py-class docutils literal notranslate"><span class="pre">callbacks.CheckpointSaver</span></code></a> and can now restore the latest
result with <a class="reference internal" href="../modules/generated/skopt.load.html#skopt.load" title="skopt.load"><code class="xref py py-class docutils literal notranslate"><span class="pre">skopt.load</span></code></a>
(see <a class="reference internal" href="store-and-load-results.html#sphx-glr-auto-examples-store-and-load-results-py"><span class="std std-ref">Store and load skopt optimization results</span></a> for more
information on that)</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skopt</span> <span class="kn">import</span> <a href="../modules/generated/skopt.load.html#skopt.load" title="skopt.load" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">load</span></a>
<span class="n">res</span> <span class="o">=</span> <a href="../modules/generated/skopt.load.html#skopt.load" title="skopt.load" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">load</span></a><span class="p">(</span><span class="s1">'./checkpoint.pkl'</span><span class="p">)</span>
<span class="n">res</span><span class="o">.</span><span class="n">fun</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-0.17524445239614728
</pre></div>
</div>
</section>
<section id="continue-the-search">
<h2>Continue the search<a class="headerlink" href="#continue-the-search" title="Permalink to this headline">¶</a></h2>
<p>The previous results can then be used to continue the optimization process:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x0</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">x_iters</span>
<span class="n">y0</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">func_vals</span>
<a href="../modules/generated/skopt.gp_minimize.html#skopt.gp_minimize" title="skopt.gp_minimize" class="sphx-glr-backref-module-skopt sphx-glr-backref-type-py-function"><span class="n">gp_minimize</span></a><span class="p">(</span><span class="n">obj_fun</span><span class="p">,</span> <span class="c1"># the function to minimize</span>
<span class="p">[(</span><span class="o">-</span><span class="mf">20.0</span><span class="p">,</span> <span class="mf">20.0</span><span class="p">)],</span> <span class="c1"># the bounds on each dimension of x</span>
<span class="n">x0</span><span class="o">=</span><span class="n">x0</span><span class="p">,</span> <span class="c1"># already examined values for x</span>
<span class="n">y0</span><span class="o">=</span><span class="n">y0</span><span class="p">,</span> <span class="c1"># observed values for x0</span>
<span class="n">acq_func</span><span class="o">=</span><span class="s2">"LCB"</span><span class="p">,</span> <span class="c1"># the acquisition function (optional)</span>
<span class="n">n_calls</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="c1"># number of evaluations of f including at x0</span>
<span class="n">n_random_starts</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="c1"># the number of random initialization points</span>
<span class="n">callback</span><span class="o">=</span><span class="p">[</span><span class="n">checkpoint_saver</span><span class="p">],</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">777</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span> fun: -0.17524445239614728
func_vals: array([-0.04682088, -0.08228249, -0.00653801, -0.07133619, 0.09063509,
0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491,
0.05448095, 0.18951609, -0.07693575, -0.14030959, -0.06324675,
-0.05588737, -0.12332314, -0.04395035, 0.09147873, 0.02650409])
models: [GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735)]
random_state: RandomState(MT19937) at 0x7F900BD12B40
space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
specs: {'args': {'func': <function obj_fun at 0x7f900af80280>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5),
n_restarts_optimizer=2, noise='gaussian',
normalize_y=True, random_state=655685735), 'n_calls': 10, 'n_random_starts': 3, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [[-20.0], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [10.524218484749973], [-17.111120867646513], [7.251301450238415], [-19.16709880491886], [-18.660711622818603], [-18.284297243496926]], 'y0': array([-0.04682088, -0.08228249, -0.00653801, -0.07133619, 0.09063509,
0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491]), 'random_state': RandomState(MT19937) at 0x7F900BD12B40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f900b0e0ee0>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
x: [-18.660711622818603]
x_iters: [[-20.0], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [10.524218484749973], [-17.111120867646513], [7.251301450238415], [-19.16709880491886], [-18.660711622818603], [-18.284297243496926], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [-19.095152570513417], [-18.99431276746093], [-19.303491085633596], [-18.902401743872336], [-18.828069913611525], [-19.391720111674047], [-18.851948436512373]]
</pre></div>
</div>
</section>
<section id="possible-problems">
<h2>Possible problems<a class="headerlink" href="#possible-problems" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p><strong>changes in search space:</strong> You can use this technique to interrupt
the search, tune the search space and continue the optimization. Note
that the optimizers will complain if <code class="docutils literal notranslate"><span class="pre">x0</span></code> contains parameter values not
covered by the dimension definitions, so in many cases shrinking the
search space will not work without deleting the offending runs from
<code class="docutils literal notranslate"><span class="pre">x0</span></code> and <code class="docutils literal notranslate"><span class="pre">y0</span></code>.</p></li>
<li><p>see <a class="reference internal" href="store-and-load-results.html#sphx-glr-auto-examples-store-and-load-results-py"><span class="std std-ref">Store and load skopt optimization results</span></a></p></li>
</ul>
<p>for more information on how the results get saved and possible caveats</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 2.554 seconds)</p>
<p><strong>Estimated memory usage:</strong> 16 MB</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-interruptible-optimization-py">
<div class="binder-badge docutils container">
<a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-optimize/scikit-optimize/master?urlpath=lab/tree/notebooks/auto_examples/interruptible-optimization.ipynb"><img alt="Launch binder" src="../_images/binder_badge_logo.svg" width="150px" /></a>
</div>
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../_downloads/283a4aef788dadc4cf49a7a2d11804bd/interruptible-optimization.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">interruptible-optimization.py</span></code></a></p>
</div>
<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
<p><a class="reference download internal" download="" href="../_downloads/673cb066bff7b44c11753f51b63cd340/interruptible-optimization.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">interruptible-optimization.ipynb</span></code></a></p>
</div>
</div>
<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
</section>
</section>
</div>
<div class="container">
<footer class="sk-content-footer">
© 2017 - 2020, scikit-optimize contributors (BSD License).
<a href="../_sources/auto_examples/interruptible-optimization.rst.txt" rel="nofollow">Show this page source</a>
</footer>
</div>
</div>
</div>
<script src="../_static/js/vendor/bootstrap.min.js"></script>
<script>
$(document).ready(function() {
/* Add a [>>>] button on the top-right corner of code sampler to hide
* the >>> and ... prompts and the output and thus make the code
* copyable. */
var div = $('.highlight-python .highlight,' +
'.highlight-python3 .highlight,' +
'.highlight-pycon .highlight,' +
'.highlight-default .highlight')
var pre = div.find('pre');
// get the styles from the current theme
pre.parent().parent().css('position', 'relative');
var hide_text = 'Hide prompts and outputs';
var show_text = 'Show prompts and outputs';
// create and add the button to all the code blocks that contain >>>
div.each(function(index) {
var jthis = $(this);
if (jthis.find('.gp').length > 0) {
var button = $('<span class="copybutton">>>></span>');
button.attr('title', hide_text);
button.data('hidden', 'false');
jthis.prepend(button);
}
// tracebacks (.gt) contain bare text elements that need to be
// wrapped in a span to work with .nextUntil() (see later)
jthis.find('pre:has(.gt)').contents().filter(function() {
return ((this.nodeType == 3) && (this.data.trim().length > 0));
}).wrap('<span>');
});
// define the behavior of the button when it's clicked
$('.copybutton').click(function(e){
e.preventDefault();
var button = $(this);
if (button.data('hidden') === 'false') {
// hide the code output
button.parent().find('.go, .gp, .gt').hide();
button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
button.css('text-decoration', 'line-through');
button.attr('title', show_text);
button.data('hidden', 'true');
} else {
// show the code output
button.parent().find('.go, .gp, .gt').show();
button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
button.css('text-decoration', 'none');
button.attr('title', hide_text);
button.data('hidden', 'false');
}
});
/*** Add permalink buttons next to glossary terms ***/
$('dl.glossary > dt[id]').append(function() {
return ('<a class="headerlink" href="#' +
this.getAttribute('id') +
'" title="Permalink to this term">¶</a>');
});
/*** Hide navbar when scrolling down ***/
// Returns true when headerlink target matches hash in url
(function() {
hashTargetOnTop = function() {
var hash = window.location.hash;
if ( hash.length < 2 ) { return false; }
var target = document.getElementById( hash.slice(1) );
if ( target === null ) { return false; }
var top = target.getBoundingClientRect().top;
return (top < 2) && (top > -2);
};
// Hide navbar on load if hash target is on top
var navBar = document.getElementById("navbar");
var navBarToggler = document.getElementById("sk-navbar-toggler");
var navBarHeightHidden = "-" + navBar.getBoundingClientRect().height + "px";
var $window = $(window);
hideNavBar = function() {
navBar.style.top = navBarHeightHidden;
};
showNavBar = function() {
navBar.style.top = "0";
}
if (hashTargetOnTop()) {
hideNavBar()
}
var prevScrollpos = window.pageYOffset;
hideOnScroll = function(lastScrollTop) {
if (($window.width() < 768) && (navBarToggler.getAttribute("aria-expanded") === 'true')) {
return;
}
if (lastScrollTop > 2 && (prevScrollpos <= lastScrollTop) || hashTargetOnTop()){
hideNavBar()
} else {
showNavBar()
}
prevScrollpos = lastScrollTop;
};
/*** high performance scroll event listener***/
var raf = window.requestAnimationFrame ||
window.webkitRequestAnimationFrame ||
window.mozRequestAnimationFrame ||
window.msRequestAnimationFrame ||
window.oRequestAnimationFrame;
var lastScrollTop = $window.scrollTop();
if (raf) {
loop();
}
function loop() {
var scrollTop = $window.scrollTop();
if (lastScrollTop === scrollTop) {
raf(loop);
return;
} else {
lastScrollTop = scrollTop;
hideOnScroll(lastScrollTop);
raf(loop);
}
}
})();
});
</script>
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
<script src="https://scikit-optimize.github.io/versionwarning.js"></script>
</body>
</html>