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Project Description
Osprey
======

|Build Status| |Coverage Status| |PyPi version| [|License|]
(http://www.apache.org/licenses/LICENSE-2.0) |DOI| [|Documentation|]
(http://msmbuilder.org/osprey)

.. figure:: http://msmbuilder.org/osprey/development/_static/osprey.svg
:alt: Logo

Logo

Osprey is an easy-to-use tool for hyperparameter optimization for
machine learning algorithms in python using scikit-learn (or using
scikit-learn compatible APIs).

Each Osprey experiment combines an dataset, an estimator, a search space
(and engine), cross validation and asynchronous serialization for
distributed parallel optimization of model hyperparameters.

Documentation
-------------

For full documentation, please visit the `Osprey
homepage <http: msmbuilder.org="" osprey=""/>`__.

Installation
------------

If you have an Anaconda Python distribution, installation is as easy as:

::

$ conda install -c omnia osprey

You can also install Osprey with ``pip``:

::

$ pip install osprey

Alternatively, you can install directly from this GitHub repo:

::

$ git clone https://github.com/msmbuilder/osprey.git
$ cd osprey && git checkout 1.1.0
$ python setup.py install

Example using `MSMBuilder <https: github.com="" msmbuilder="" msmbuilder="">`__
-----------------------------------------------------------------------

Below is an example of an osprey ``config`` file to cross validate
Markov state models based on varying the number of clusters and dihedral
angles used in a model:

.. code:: yaml

estimator:
eval_scope: msmbuilder
eval: |
Pipeline([
('featurizer', DihedralFeaturizer(types=['phi', 'psi'])),
('cluster', MiniBatchKMeans()),
('msm', MarkovStateModel(n_timescales=5, verbose=False)),
])

search_space:
cluster__n_clusters:
min: 10
max: 100
type: int
featurizer__types:
choices:
- ['phi', 'psi']
- ['phi', 'psi', 'chi1']
type: enum

cv: 5

dataset_loader:
name: mdtraj
params:
trajectories: ~/local/msmbuilder/Tutorial/XTC/*/*.xtc
topology: ~/local/msmbuilder/Tutorial/native.pdb
stride: 1

trials:
uri: sqlite:///osprey-trials.db

Then run ``osprey worker``. You can run multiple parallel instances of
``osprey worker`` simultaneously on a cluster too.

::

$ osprey worker config.yaml

...

----------------------------------------------------------------------
Beginning iteration 1 / 1
----------------------------------------------------------------------
History contains: 0 trials
Choosing next hyperparameters with random...
{'cluster__n_clusters': 20, 'featurizer__types': ['phi', 'psi']}

Fitting 5 folds for each of 1 candidates, totalling 5 fits
[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.8s finished
---------------------------------
Success! Model score = 4.080646
(best score so far = 4.080646)
---------------------------------

1/1 models fit successfully.
time: October 27, 2014 10:44 PM
elapsed: 4 seconds.
osprey worker exiting.

You can dump the database to JSON or CSV with ``osprey dump``.

Dependencies
------------

- ``python>=2.7.11``
- ``six>=1.10.0``
- ``pyyaml>=3.11``
- ``numpy>=1.10.4``
- ``scipy>=0.17.0``
- ``scikit-learn>=0.17.0``
- ``sqlalchemy>=1.0.10``
- ``bokeh>=0.12.0``
- ``matplotlib>=1.5.0``
- ``pandas>=0.18.0``
- ``GPy`` (optional, required for ``gp`` strategy)
- ``hyperopt`` (optional, required for ``hyperopt_tpe`` strategy)
- ``nose`` (optional, for testing)

Contributing
------------

In case you encounter any issues with this package, please consider
submitting a ticket to the `GitHub Issue
Tracker <https: github.com="" msmbuilder="" osprey="" issues="">`__. We also
welcome any feature requests and highly encourage users to `submit pull
requests <https: help.github.com="" articles="" creating-a-pull-request=""/>`__
for bug fixes and improvements.

For more detailed information, please refer to our
`documentation <http: msmbuilder.org="" osprey="" contributing.html="">`__.

Citing
------

If you use Osprey in your research, please cite:

.. code:: bibtex

@misc{osprey,
author = {Robert T. McGibbon and
Carlos X. Hernández and
Matthew P. Harrigan and
Steven Kearnes and
Mohammad M. Sultan and
Stanislaw Jastrzebski and
Brooke E. Husic and
Vijay S. Pande},
title = {Osprey 1.0.0},
month = jun,
year = 2016,
doi = {10.5281/zenodo.56251},
url = {http://dx.doi.org/10.5281/zenodo.56251}
}

.. |Build Status| image:: https://travis-ci.org/msmbuilder/osprey.svg?branch=master
:target: https://travis-ci.org/msmbuilder/osprey
.. |Coverage Status| image:: https://coveralls.io/repos/github/msmbuilder/osprey/badge.svg?branch=master
:target: https://coveralls.io/github/msmbuilder/osprey?branch=master
.. |PyPi version| image:: https://badge.fury.io/py/osprey.svg
:target: https://pypi.python.org/pypi/osprey/
.. |License| image:: https://img.shields.io/badge/license-ASLv2.0-red.svg?style=flat
.. |DOI| image:: https://zenodo.org/badge/9890/msmbuilder/osprey.svg
:target: https://zenodo.org/badge/latestdoi/9890/msmbuilder/osprey
.. |Documentation| image:: https://img.shields.io/badge/docs-latest-blue.svg?style=flat
Release History

Release History

1.1.0

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Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
osprey-1.1.0.tar.gz (39.8 kB) Copy SHA256 Checksum SHA256 Source Sep 8, 2016

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