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# Interruptible optimization runs with checkpoints

Christian Schell, Mai 2018

import numpy as np
np.random.seed(777)


## 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 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

## Simple example

We will use pretty much the same optimization problem as in the bayesian-optimization.ipynb notebook. Additionaly we will instantiate the CheckpointSaver and pass it to the minimizer:

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,                   # the number of evaluations of f including at x0
n_random_starts=0,           # the number of random initialization points
callback=[checkpoint_saver], # a list of callbacks including the checkpoint saver
random_state=777);

/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "


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?

TL;DR: gp_minimize did not finish, and there is no res variable with the actual results!

## Restoring the last checkpoint

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

from skopt import load

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

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,         # the number of evaluations of f including at x0
n_random_starts=0,  # the number of random initialization points
callback=[checkpoint_saver],
random_state=777);

/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "
/home/ubuntu/scikit-optimize/skopt/optimizer/optimizer.py:399: UserWarning: The objective has been evaluated at this point before.
warnings.warn("The objective has been evaluated "


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