Metadata-Version: 1.1
Name: osprey
Version: 1.1.0
Summary: |Build Status| |Coverage Status| |PyPi version| [|License|]
(http://www.apache.org/licenses/LICENSE-2.0) |DOI| [|Documentation|]
(http://msmbuilder.org/osprey)
Home-page: https://github.com/msmbuilder/osprey
Author: Robert T. McGibbon
Author-email: rmcgibbo@gmail.com
License: Apache Software License
Download-URL: https://pypi.python.org/pypi/osprey/
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
        
Platform: Windows
Platform: Linux
Platform: Mac OS-X
Platform: Unix
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
