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aalto-boss-1.6.3
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Bayesian optimization structure search
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | aalto-boss-1.6.3 |
| نام | aalto-boss |
| نسخه کتابخانه | 1.6.3 |
| نگهدارنده | ['The BOSS developers team'] |
| ایمیل نگهدارنده | ['milica.todorovic@utu.fi'] |
| نویسنده | Ville Parkkinen, Henri Paulamaki, Arttu Tolvanen, Ulpu Remes, Nuutti Sten, Emma Lehto, Manuel Kuchelmeister, Tuomas Rossi, Mikael Granit" "Joakim Loefgren, Milica Todorovic, |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://gitlab.com/cest-group/boss |
| آدرس اینترنتی | https://pypi.org/project/aalto-boss/ |
| مجوز | Apache License 2.0 |
BOSS
=========
Bayesian Optimization Structure Search (BOSS) is a general-purpose Bayesian Optimization code. It is designed to facilitate machine learning in computational and experimental natural sciences.
For a more detailed description of the code and tutorials, please consult the `user guide <https://cest-group.gitlab.io/boss>`_.
Installation
------------
BOSS is distributed as a PyPI package and can be installed using pip::
python3 -m pip install aalto-boss
We recommend installing BOSS inside a virtual environment (``venv``, ``conda``...). If you are not using virtual environments, we recommend performing a user-installation instead::
python3 -m pip install --user aalto-boss
Basic usage
-----------
As an easy example, consider the optimization of a bounded 1D function. BOSS can be run either directly from Python or via a CLI interface, both these approaches are illustrated briefly below. Note that BOSS always minimizes a given function.
Python iterface
^^^^^^^^^^^^^^^^^^^^^
To run BOSS from Python we first define our objective function, by default BOSS expects this function to take a single 2D numpy array as argument (this behaviour can be modified) and return a scalar value. Next, we import the ``BOMain`` object and feed it the function plus any number of BOSS keywords, after which the optimization can be started. Once finished, the optimziation results are returned in a ``BOResults`` object.
.. code-block:: python
""" Using BOSS to solve the minimization problem
f(x) = sin(x) + 1.5*exp(-(x-4.3)**2) , 0 < x < 7
"""
import numpy as np
from boss.bo.bo_main import BOMain
from boss.pp.pp_main import PPMain
def func(X):
""" BOSS-compatible definition of the function. """
x = X[0, 0]
return np.sin(x) + 1.5*np.exp(-(x - 4.3)**2)
if __name__ == '__main__':
bo = BOMain(
func,
np.array([[0., 7.]]), # bounds
yrange=[-1, 1],
kernel='rbf',
initpts=5,
iterpts=15,
)
res = bo.run()
# print global minimum value and location from the last iteration
print(res.select('mu_glmin', -1), res.select('x_glmin', -1))
# run BOSS post-processing
# creates a folder "postprocessing" with plots and data
pp = PPMain(res, pp_models=True, pp_acq_funcs=True)
pp.run()
Command-line iterface
^^^^^^^^^^^^^^^^^^^^^
The CLI is provided by an executable called ``boss``. The user must provide an input file containing a list of BOSS keywords and a separate Python script that defines a function to be optimized. By default, BOSS expects this function to take a single 2D numpy array as argument (this behaviour can be modified) and return a scalar value. Below we define such a function in a Python script, arbitrarily named ``user_function.py``:
.. code-block:: python
""" user_function.py
This script contains the function definition for the minimization problem
f(x) = sin(x) + 1.5*exp(-(x-4.3)**2) , 0 < x < 7
Note that the bounds are specified in the BOSS input file.
"""
import numpy as np
def func(X):
""" BOSS-compatible definition of the function. """
x = X[0, 0]
return np.sin(x) + 1.5*np.exp(-(x - 4.3)**2)
To minimize this function subject to the constraint *0 < x < 7*, we define a BOSS input file ``boss.in``:
.. code-block:: python
# boss.in
userfn user_function.py func
bounds 0 7
yrange -1 1
kernel rbf
initpts 5
iterpts 15
The optimization (including post-processing) can now be started from the command line:
.. code-block:: bash
$ boss op boss.in
Credits
-------
BOSS is under active development in the `Materials Informatics Laboratory` at the University of Turku and the `Computational Electronic Structure Theory (CEST) group <http://cest.aalto.fi/>`_ at Aalto University. Past and present members of development team include
* Ville Parkkinen
* Henri Paulamäki
* Arttu Tolvanen
* Ulpu Remes
* Nuutti Sten
* Emma Lehto
* Tuomas Rossi
* Manuel Kuchelmeister
* Mikael Granit
* Joakim Löfgren (maintainer)
* Milica Todorović (team lead)
If you wish to use BOSS in your research, please cite
| Milica Todorovic, Micheal U. Gutmann, Jukka Corander, and Patrick Rinke
| *Bayesian inference of atomistic structure in functional materials*
| npj Comput Mater **5**, 35 (2019)
| `doi: 10.1038/s41524-019-0175-2 <https://doi.org/10.1038/s41524-019-0175-2>`_
Issues and feature requests
---------------------------
It is strongly encouraged to submit bug reports, questions, and feature requests via the
`gitlab issue tracker <https://gitlab.com/cest-group/boss/issues>`_.
The BOSS development team can be contacted by email at milica.todorovic@utu.fi
نیازمندی
| مقدار | نام |
|---|---|
| - | GPy |
| >=3.0 | matplotlib |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl aalto-boss-1.6.3:
pip install aalto-boss-1.6.3.whl
نصب پکیج tar.gz aalto-boss-1.6.3:
pip install aalto-boss-1.6.3.tar.gz