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.. "auto_examples\interruptible-optimization.py"
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.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_interruptible-optimization.py>`
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.. 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>`

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      :download:`Download Python source code: interruptible-optimization.py <interruptible-optimization.py>`


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 .. rst-class:: sphx-glr-signature

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