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store-and-load-results.py
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store-and-load-results.py
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"""
===========================================
Store and load `skopt` optimization results
===========================================
Mikhail Pak, October 2016.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
Problem statement
=================
We often want to store optimization results in a file. This can be useful,
for example,
* if you want to share your results with colleagues;
* if you want to archive and/or document your work;
* or if you want to postprocess your results in a different Python instance or on an another computer.
The process of converting an object into a byte stream that can be stored in
a file is called _serialization_.
Conversely, _deserialization_ means loading an object from a byte stream.
**Warning:** Deserialization is not secure against malicious or erroneous
code. Never load serialized data from untrusted or unauthenticated sources!
"""
print(__doc__)
import numpy as np
import os
import sys
#############################################################################
# Simple example
# ==============
#
# We will use the same optimization problem as in the
# :ref:`sphx_glr_auto_examples_bayesian-optimization.py` notebook:
from skopt import gp_minimize
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
res = gp_minimize(obj_fun, # the function to minimize
[(-2.0, 2.0)], # the bounds on each dimension of x
x0=[0.], # the starting point
acq_func="LCB", # the acquisition function (optional)
n_calls=15, # the number of evaluations of f including at x0
n_random_starts=3, # the number of random initial points
random_state=777)
#############################################################################
# As long as your Python session is active, you can access all the
# optimization results via the `res` object.
#
# So how can you store this data in a file? `skopt` conveniently provides
# functions :class:`skopt.dump` and :class:`skopt.load` that handle this for you.
# These functions are essentially thin wrappers around the
# `joblib <https://joblib.readthedocs.io/en/latest/>`_ module's :obj:`joblib.dump` and :obj:`joblib.load`.
#
# We will now show how to use :class:`skopt.dump` and :class:`skopt.load` for storing
# and loading results.
#
# Using `skopt.dump()` and `skopt.load()`
# =======================================
#
# For storing optimization results into a file, call the :class:`skopt.dump`
# function:
from skopt import dump, load
dump(res, 'result.pkl')
#############################################################################
# And load from file using :class:`skopt.load`:
res_loaded = load('result.pkl')
res_loaded.fun
#############################################################################
# You can fine-tune the serialization and deserialization process by calling
# :class:`skopt.dump` and :class:`skopt.load` with additional keyword arguments. See the
# `joblib <https://joblib.readthedocs.io/en/latest/>`_ documentation
# :obj:`joblib.dump` and
# :obj:`joblib.load` for the additional parameters.
#
# For instance, you can specify the compression algorithm and compression
# level (highest in this case):
dump(res, 'result.gz', compress=9)
from os.path import getsize
print('Without compression: {} bytes'.format(getsize('result.pkl')))
print('Compressed with gz: {} bytes'.format(getsize('result.gz')))
#############################################################################
# Unserializable objective functions
# ----------------------------------
#
# Notice that if your objective function is non-trivial (e.g. it calls MATLAB
# engine from Python), it might be not serializable and :class:`skopt.dump` will
# raise an exception when you try to store the optimization results.
# In this case you should disable storing the objective function by calling
# :class:`skopt.dump` with the keyword argument `store_objective=False`:
dump(res, 'result_without_objective.pkl', store_objective=False)
#############################################################################
# Notice that the entry `'func'` is absent in the loaded object but is still
# present in the local variable:
res_loaded_without_objective = load('result_without_objective.pkl')
print('Loaded object: ', res_loaded_without_objective.specs['args'].keys())
print('Local variable:', res.specs['args'].keys())
#############################################################################
# Possible problems
# =================
#
# * **Python versions incompatibility:** In general, objects serialized in
# Python 2 cannot be deserialized in Python 3 and vice versa.
# * **Security issues:** Once again, do not load any files from untrusted
# sources.
# * **Extremely large results objects:** If your optimization results object
#
# is extremely large, calling :class:`skopt.dump` with `store_objective=False` might
# cause performance issues. This is due to creation of a deep copy without the
# objective function. If the objective function it is not critical to you, you
# can simply delete it before calling :class:`skopt.dump`. In this case, no deep
# copy is created:
del res.specs['args']['func']
dump(res, 'result_without_objective_2.pkl')