Package: osprey
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(http://www.apache.org/licenses/LICENSE-2.0)[]
(http://msmbuilder.org/osprey)LogoOsprey 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.DocumentationFor full documentation, please visit theOsprey
homepage.InstallationIf you have an Anaconda Python distribution, installation is as easy as:$ conda install -c omnia ospreyYou can also install Osprey withpip:$ pip install ospreyAlternatively, 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 installExample usingMSMBuilderBelow is an example of an ospreyconfigfile to cross validate
Markov state models based on varying the number of clusters and dihedral
angles used in a model:estimator:eval_scope:msmbuildereval:|Pipeline([('featurizer', DihedralFeaturizer(types=['phi', 'psi'])),('cluster', MiniBatchKMeans()),('msm', MarkovStateModel(n_timescales=5, verbose=False)),])search_space:cluster__n_clusters:min:10max:100type:intfeaturizer__types:choices:-['phi','psi']-['phi','psi','chi1']type:enumcv:5dataset_loader:name:mdtrajparams:trajectories:~/local/msmbuilder/Tutorial/XTC/*/*.xtctopology:~/local/msmbuilder/Tutorial/native.pdbstride:1trials:uri:sqlite:///osprey-trials.dbThen runosprey worker. You can run multiple parallel instances ofosprey workersimultaneously on a cluster too.$ osprey worker config.yaml

...

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Beginning iteration                                              1 / 1
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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 withosprey dump.Dependenciespython>=2.7.11six>=1.10.0pyyaml>=3.11numpy>=1.10.4scipy>=0.17.0scikit-learn>=0.17.0sqlalchemy>=1.0.10bokeh>=0.12.0matplotlib>=1.5.0pandas>=0.18.0GPy(optional, required forgpstrategy)hyperopt(optional, required forhyperopt_tpestrategy)nose(optional, for testing)ContributingIn case you encounter any issues with this package, please consider
submitting a ticket to theGitHub Issue
Tracker. We also
welcome any feature requests and highly encourage users tosubmit pull
requestsfor bug fixes and improvements.For more detailed information, please refer to ourdocumentation.CitingIf you use Osprey in your research, please cite:@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}}