scikit-optimize-fix 0.9.1

Description:

scikitoptimizefix 0.9.1

Scikit-Optimize
Scikit-Optimize, or skopt, is a simple and efficient library to
minimize (very) expensive and noisy black-box functions. It implements
several methods for sequential model-based optimization. skopt aims
to be accessible and easy to use in many contexts.
The library is built on top of NumPy, SciPy and Scikit-Learn.
We do not perform gradient-based optimization. For gradient-based
optimization algorithms look at
scipy.optimize
here.



Approximated objective function after 50 iterations of gp_minimize.
Plot made using skopt.plots.plot_objective.

Important links

Static documentation - Static
documentation
Example notebooks - can be found in examples.
Issue tracker -
https://github.com/scikit-optimize/scikit-optimize/issues
Releases - https://pypi.python.org/pypi/scikit-optimize



Install
scikit-optimize requires

Python >= 3.6
NumPy (>= 1.13.3)
SciPy (>= 0.19.1)
joblib (>= 0.11)
scikit-learn >= 0.20
matplotlib >= 2.0.0

You can install the latest release with:
pip install scikit-optimize
This installs an essential version of scikit-optimize. To install scikit-optimize
with plotting functionality, you can instead do:
pip install 'scikit-optimize[plots]'
This will install matplotlib along with scikit-optimize.
In addition there is a conda-forge package
of scikit-optimize:
conda install -c conda-forge scikit-optimize
Using conda-forge is probably the easiest way to install scikit-optimize on
Windows.


Getting started
Find the minimum of the noisy function f(x) over the range
-2 < x < 2 with skopt:
import numpy as np
from skopt import gp_minimize

def f(x):
return (np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) +
np.random.randn() * 0.1)

res = gp_minimize(f, [(-2.0, 2.0)])
For more control over the optimization loop you can use the skopt.Optimizer
class:
from skopt import Optimizer

opt = Optimizer([(-2.0, 2.0)])

for i in range(20):
suggested = opt.ask()
y = f(suggested)
opt.tell(suggested, y)
print('iteration:', i, suggested, y)
Read our introduction to bayesian
optimization
and the other examples.


Development
The library is still experimental and under heavy development. Checkout
the next
milestone
for the plans for the next release or look at some easy
issues
to get started contributing.
The development version can be installed through:
git clone https://github.com/scikit-optimize/scikit-optimize.git
cd scikit-optimize
pip install -e.
Run all tests by executing pytest in the top level directory.
To only run the subset of tests with short run time, you can use pytest -m 'fast_test' (pytest -m 'slow_test' is also possible). To exclude all slow running tests try pytest -m 'not slow_test'.
This is implemented using pytest attributes. If a tests runs longer than 1 second, it is marked as slow, else as fast.
All contributors are welcome!

Making a Release
The release procedure is almost completely automated. By tagging a new release
travis will build all required packages and push them to PyPI. To make a release
create a new issue and work through the following checklist:

update the version tag in __init__.py
update the version tag mentioned in the README
check if the dependencies in setup.py are valid or need unpinning
check that the doc/whats_new/v0.X.rst is up to date
did the last build of master succeed?
create a new release
ping conda-forge

Before making a release we usually create a release candidate. If the next
release is v0.X then the release candidate should be tagged v0.Xrc1 in
__init__.py. Mark a release candidate as a “pre-release”
on GitHub when you tag it.



Commercial support
Feel free to get in touch if you need commercial
support or would like to sponsor development. Resources go towards paying
for additional work by seasoned engineers and researchers.


Made possible by
The scikit-optimize project was made possible with the support of




If your employer allows you to work on scikit-optimize during the day and would like
recognition, feel free to add them to the “Made possible by” list.