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.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\interruptible-optimization.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_auto_examples_interruptible-optimization.py>`
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
.. GENERATED FROM PYTHON SOURCE LINES 29-35
.. code-block:: Python
print(__doc__)
import numpy as np
np.random.seed(777)
.. GENERATED FROM PYTHON SOURCE LINES 36-43
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:
.. GENERATED FROM PYTHON SOURCE LINES 43-68
.. code-block:: Python
from skopt import gp_minimize
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) # kwargs passed to `skopt.dump`
gp_minimize(
obj_fun, # the function to minimize
[(-20.0, 20.0)], # the bounds on each dimension of x
x0=[-20.0], # 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
.. code-block:: none
fun: -0.17524445239614728
x: [-18.660711608230713]
func_vals: [-4.682e-02 -8.228e-02 -6.538e-03 -7.134e-02 9.064e-02
7.662e-02 8.261e-02 -1.324e-01 -1.752e-01 1.002e-01]
x_iters: [[-20.0], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [10.524218484747195], [-17.111120867646253], [7.251301457256783], [-19.16709880389749], [-18.660711608230713], [-18.28429723556215]]
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)]
space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
random_state: RandomState(MT19937)
specs: args: func: <function obj_fun at 0x0000020BCE5B9940>
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)
verbose: False
callback: [<skopt.callbacks.CheckpointSaver object at 0x0000020BCF20E710>]
n_points: 10000
n_restarts_optimizer: 5
xi: 0.01
kappa: 1.96
n_jobs: 1
model_queue_size: None
space_constraint: None
function: base_minimize
.. GENERATED FROM PYTHON SOURCE LINES 69-86
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)
.. GENERATED FROM PYTHON SOURCE LINES 86-93
.. code-block:: Python
from skopt import load
res = load('./checkpoint.pkl')
res.fun
.. rst-class:: sphx-glr-script-out
.. code-block:: none
-0.17524445239614728
.. GENERATED FROM PYTHON SOURCE LINES 94-98
Continue the search
===================
The previous results can then be used to continue the optimization process:
.. GENERATED FROM PYTHON SOURCE LINES 98-120
.. code-block:: Python
x0 = res.x_iters
y0 = res.func_vals
# To ensure that the base estimator is loaded properly and that the next
# parameters are new ones:
base_estimator = res.specs['args']['base_estimator']
random_state = res.random_state
gp_minimize(
obj_fun, # the function to minimize
[(-20.0, 20.0)], # the bounds on each dimension of x
base_estimator=base_estimator, # warm-started base-estimator from checkpoint
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=random_state,
)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
fun: -0.17524445239614728
x: [-18.660711608230713]
func_vals: [-4.682e-02 -8.228e-02 ... 9.148e-02 2.650e-02]
x_iters: [[-20.0], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [10.524218484747195], [-17.111120867646253], [7.251301457256783], [-19.16709880389749], [-18.660711608230713], [-18.28429723556215], [-4.89536560655587], [-13.817289687225252], [6.9752035588202865], [-19.137121427347815], [-18.893981612139648], [-19.36136150292367], [-18.95171384338072], [6.383980575487712], [-18.82159785473173], [-19.373206975288387]]
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)]
space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
random_state: RandomState(MT19937)
specs: args: func: <function obj_fun at 0x0000020BCE5B9940>
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], [10.524218484747195], [-17.111120867646253], [7.251301457256783], [-19.16709880389749], [-18.660711608230713], [-18.28429723556215]]
y0: [-4.682e-02 -8.228e-02
-6.538e-03 -7.134e-02
9.064e-02 7.662e-02
8.261e-02 -1.324e-01
-1.752e-01 1.002e-01]
random_state: RandomState(MT19937)
verbose: False
callback: [<skopt.callbacks.CheckpointSaver object at 0x0000020BCF20E710>]
n_points: 10000
n_restarts_optimizer: 5
xi: 0.01
kappa: 1.96
n_jobs: 1
model_queue_size: None
space_constraint: None
function: base_minimize
.. GENERATED FROM PYTHON SOURCE LINES 121-133
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 1.988 seconds)
.. _sphx_glr_download_auto_examples_interruptible-optimization.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/holgern/scikit-optimize/master?urlpath=lab/tree/notebooks/auto_examples/interruptible-optimization.ipynb
:alt: Launch binder
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: interruptible-optimization.ipynb <interruptible-optimization.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: interruptible-optimization.py <interruptible-optimization.py>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
|
