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"info": {
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"description": "Osprey\n======\n\n|Build Status| |Coverage Status| |PyPi version| [|License|]\n(http://www.apache.org/licenses/LICENSE-2.0) |DOI| [|Documentation|]\n(http://msmbuilder.org/osprey)\n\n.. figure:: http://msmbuilder.org/osprey/development/_static/osprey.svg\n :alt: Logo\n\n Logo\n\nOsprey is an easy-to-use tool for hyperparameter optimization for\nmachine learning algorithms in python using scikit-learn (or using\nscikit-learn compatible APIs).\n\nEach Osprey experiment combines an dataset, an estimator, a search space\n(and engine), cross validation and asynchronous serialization for\ndistributed parallel optimization of model hyperparameters.\n\nDocumentation\n-------------\n\nFor full documentation, please visit the `Osprey\nhomepage `__.\n\nInstallation\n------------\n\nIf you have an Anaconda Python distribution, installation is as easy as:\n\n::\n\n $ conda install -c omnia osprey\n\nYou can also install Osprey with ``pip``:\n\n::\n\n $ pip install osprey\n\nAlternatively, you can install directly from this GitHub repo:\n\n::\n\n $ git clone https://github.com/msmbuilder/osprey.git\n $ cd osprey && git checkout 1.1.0\n $ python setup.py install\n\nExample using `MSMBuilder `__\n-----------------------------------------------------------------------\n\nBelow is an example of an osprey ``config`` file to cross validate\nMarkov state models based on varying the number of clusters and dihedral\nangles used in a model:\n\n.. code:: yaml\n\n estimator:\n eval_scope: msmbuilder\n eval: |\n Pipeline([\n ('featurizer', DihedralFeaturizer(types=['phi', 'psi'])),\n ('cluster', MiniBatchKMeans()),\n ('msm', MarkovStateModel(n_timescales=5, verbose=False)),\n ])\n\n search_space:\n cluster__n_clusters:\n min: 10\n max: 100\n type: int\n featurizer__types:\n choices:\n - ['phi', 'psi']\n - ['phi', 'psi', 'chi1']\n type: enum\n\n cv: 5\n\n dataset_loader:\n name: mdtraj\n params:\n trajectories: ~/local/msmbuilder/Tutorial/XTC/*/*.xtc\n topology: ~/local/msmbuilder/Tutorial/native.pdb\n stride: 1\n\n trials:\n uri: sqlite:///osprey-trials.db\n\nThen run ``osprey worker``. You can run multiple parallel instances of\n``osprey worker`` simultaneously on a cluster too.\n\n::\n\n $ osprey worker config.yaml\n\n ...\n\n ----------------------------------------------------------------------\n Beginning iteration 1 / 1\n ----------------------------------------------------------------------\n History contains: 0 trials\n Choosing next hyperparameters with random...\n {'cluster__n_clusters': 20, 'featurizer__types': ['phi', 'psi']}\n\n Fitting 5 folds for each of 1 candidates, totalling 5 fits\n [Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.3s\n [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.8s finished\n ---------------------------------\n Success! Model score = 4.080646\n (best score so far = 4.080646)\n ---------------------------------\n\n 1/1 models fit successfully.\n time: October 27, 2014 10:44 PM\n elapsed: 4 seconds.\n osprey worker exiting.\n\nYou can dump the database to JSON or CSV with ``osprey dump``.\n\nDependencies\n------------\n\n- ``python>=2.7.11``\n- ``six>=1.10.0``\n- ``pyyaml>=3.11``\n- ``numpy>=1.10.4``\n- ``scipy>=0.17.0``\n- ``scikit-learn>=0.17.0``\n- ``sqlalchemy>=1.0.10``\n- ``bokeh>=0.12.0``\n- ``matplotlib>=1.5.0``\n- ``pandas>=0.18.0``\n- ``GPy`` (optional, required for ``gp`` strategy)\n- ``hyperopt`` (optional, required for ``hyperopt_tpe`` strategy)\n- ``nose`` (optional, for testing)\n\nContributing\n------------\n\nIn case you encounter any issues with this package, please consider\nsubmitting a ticket to the `GitHub Issue\nTracker `__. We also\nwelcome any feature requests and highly encourage users to `submit pull\nrequests `__\nfor bug fixes and improvements.\n\nFor more detailed information, please refer to our\n`documentation `__.\n\nCiting\n------\n\nIf you use Osprey in your research, please cite:\n\n.. code:: bibtex\n\n @misc{osprey,\n author = {Robert T. McGibbon and\n Carlos X. Hern\u00e1ndez and\n Matthew P. Harrigan and\n Steven Kearnes and\n Mohammad M. Sultan and\n Stanislaw Jastrzebski and\n Brooke E. Husic and\n Vijay S. Pande},\n title = {Osprey 1.0.0},\n month = jun,\n year = 2016,\n doi = {10.5281/zenodo.56251},\n url = {http://dx.doi.org/10.5281/zenodo.56251}\n }\n\n.. |Build Status| image:: https://travis-ci.org/msmbuilder/osprey.svg?branch=master\n :target: https://travis-ci.org/msmbuilder/osprey\n.. |Coverage Status| image:: https://coveralls.io/repos/github/msmbuilder/osprey/badge.svg?branch=master\n :target: https://coveralls.io/github/msmbuilder/osprey?branch=master\n.. |PyPi version| image:: https://badge.fury.io/py/osprey.svg\n :target: https://pypi.python.org/pypi/osprey/\n.. |License| image:: https://img.shields.io/badge/license-ASLv2.0-red.svg?style=flat\n.. |DOI| image:: https://zenodo.org/badge/9890/msmbuilder/osprey.svg\n :target: https://zenodo.org/badge/latestdoi/9890/msmbuilder/osprey\n.. |Documentation| image:: https://img.shields.io/badge/docs-latest-blue.svg?style=flat",
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