1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
|
"""
===========================================
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
from skopt import gp_minimize
#############################################################################
# Simple example
# ==============
#
# We will use the same optimization problem as in the
# :ref:`sphx_glr_auto_examples_bayesian-optimization.py` notebook:
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=[0.0], # 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')
|
