.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_interruptible-optimization.py:
================================================
Interruptible optimization runs with checkpoints
================================================
Christian Schell, Mai 2018
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
Problem statement
=================
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.
With the help of the :class:`callbacks.CheckpointSaver` callback the optimizer's current state
can be saved after each iteration, allowing to restart from that point at any
time.
This is useful, for example,
* if you don't know how long the process will take and cannot hog computational resources forever
* if there might be system failures due to shaky infrastructure (or colleagues...)
* if you want to adjust some parameters and continue with the already obtained results
.. code-block:: default
print(__doc__)
import sys
import numpy as np
np.random.seed(777)
import os
Simple example
==============
We will use pretty much the same optimization problem as in the
:ref:`sphx_glr_auto_examples_bayesian-optimization.py`
notebook. Additionally we will instantiate the :class:`callbacks.CheckpointSaver`
and pass it to the minimizer:
.. code-block:: default
from skopt import gp_minimize
from skopt import callbacks
from skopt.callbacks import CheckpointSaver
noise_level = 0.1
def obj_fun(x, noise_level=noise_level):
return np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) + np.random.randn() \
* noise_level
checkpoint_saver = CheckpointSaver("./checkpoint.pkl", compress=9) # keyword arguments will be passed to `skopt.dump`
gp_minimize(obj_fun, # the function to minimize
[(-20.0, 20.0)], # the bounds on each dimension of x
x0=[-20.], # the starting point
acq_func="LCB", # the acquisition function (optional)
n_calls=10, # number of evaluations of f including at x0
n_random_starts=3, # the number of random initial points
callback=[checkpoint_saver],
# a list of callbacks including the checkpoint saver
random_state=777)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
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 0x7F4692427340
space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
specs: {'args': {'func': , '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 0x7F4692427340, 'verbose': False, 'callback': [], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
x: [-18.660711608231072]
x_iters: [[-20.0], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [10.52421848474863], [-17.111120867645933], [7.251301457257323], [-19.167098803897993], [-18.660711608231072], [-18.284297234995442]]
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?
:class:`gp_minimize` did not finish, and there is no `res` variable with the
actual results!
Restoring the last checkpoint
=============================
Luckily we employed the :class:`callbacks.CheckpointSaver` and can now restore the latest
result with :class:`skopt.load`
(see :ref:`sphx_glr_auto_examples_store-and-load-results.py` for more
information on that)
.. code-block:: default
from skopt import load
res = load('./checkpoint.pkl')
res.fun
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
-0.17524445239614728
Continue the search
===================
The previous results can then be used to continue the optimization process:
.. code-block:: default
x0 = res.x_iters
y0 = res.func_vals
gp_minimize(obj_fun, # the function to minimize
[(-20.0, 20.0)], # the bounds on each dimension of x
x0=x0, # already examined values for x
y0=y0, # observed values for x0
acq_func="LCB", # the acquisition function (optional)
n_calls=10, # number of evaluations of f including at x0
n_random_starts=3, # the number of random initialization points
callback=[checkpoint_saver],
random_state=777)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
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 0x7F4691BFC340
space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
specs: {'args': {'func': , '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], [10.52421848474863], [-17.111120867645933], [7.251301457257323], [-19.167098803897993], [-18.660711608231072], [-18.284297234995442]], '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 0x7F4691BFC340, 'verbose': False, 'callback': [], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
x: [-18.660711608231072]
x_iters: [[-20.0], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [10.52421848474863], [-17.111120867645933], [7.251301457257323], [-19.167098803897993], [-18.660711608231072], [-18.284297234995442], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [-19.095152555752314], [-18.994312771398576], [-19.303491079248285], [-18.90240173642124], [-18.828069903081253], [-19.391720113476794], [-18.851948428477716]]
Possible problems
=================
* **changes in search space:** You can use this technique to interrupt
the search, tune the search space and continue the optimization. Note
that the optimizers will complain if `x0` 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
`x0` and `y0`.
* see :ref:`sphx_glr_auto_examples_store-and-load-results.py`
for more information on how the results get saved and possible caveats
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 2.810 seconds)
**Estimated memory usage:** 14 MB
.. _sphx_glr_download_auto_examples_interruptible-optimization.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: /../../miniconda/envs/testenv/lib/python3.8/site-packages/sphinx_gallery/_static/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-optimize/scikit-optimize/master?urlpath=lab/tree/notebooks/auto_examples/interruptible-optimization.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: interruptible-optimization.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: interruptible-optimization.ipynb `
.. only:: html
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