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# Interruptible optimization runs with checkpoints

Christian Schell, Mai 2018 Reformatted by Holger Nahrstaedt 2020

## 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 `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

    print(__doc__)
    import numpy as np

    np.random.seed(777)

## Simple example

We will use pretty much the same optimization problem as in the Bayesian optimization with skopt notebook. Additionally we will instantiate the `callbacks.CheckpointSaver` and pass it to the minimizer:

    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,
    )

             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

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?

`gp_minimize` did not finish, and there is no `res` variable with the actual results!

## Restoring the last checkpoint

Luckily we employed the `callbacks.CheckpointSaver` and can now restore the latest result with `skopt.load` (see Store and load skopt optimization results for more information on that)

    from skopt import load

    res = load('./checkpoint.pkl')

    res.fun

    -0.17524445239614728

## Continue the search

The previous results can then be used to continue the optimization process:

    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,
    )

             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

## 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 Store and load skopt optimization results

for more information on how the results get saved and possible caveats

Total running time of the script: (0 minutes 1.988 seconds)

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