Metadata-Version: 1.1
Name: osprey
Version: 0.3
Summary: 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).
Home-page: https://github.com/rmcgibbo/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| |PyPi version| |Supported Python versions| |License|
        ==========================================================================
        
        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.
        
        Example (with `mixtape <https://github.com/rmcgibbo/mixtape>`__ models/datasets)
        --------------------------------------------------------------------------------
        
        ::
        
            $ cat config.yaml
            estimator:
              eval_scope: mixtape
              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`` simultaniously on a cluster too.
        
        ::
        
            $ osprey worker config.yaml
            ======================================================================
            = osprey is a tool for machine learning hyperparameter optimization. =
            ======================================================================
        
            osprey version:  0.2_10_g18392d9_dirty-py2.7.egg
            time:            October 27, 2014 10:44 PM
            hostname:        dn0a230538.sunet
            cwd:             /private/var/folders/yb/vpt17lxs67vf02qpvgvjrc5m0000gn/T/tmpDgBwlU
            pid:             99407
        
            Loading config file:     config.yaml...
            Loading trials database: sqlite:///osprey-trials.db (table = "trials")...
        
            Loading dataset...
              100 elements without labels
            Instantiated estimator:
              Pipeline(steps=[('featurizer', DihedralFeaturizer(sincos=True, types=['phi', 'psi'])), ('tica', tICA(gamma=0.05, lag_time=1, n_components=4, weighted_transform=False)), ('cluster', MiniBatchKMeans(batch_size=100, compute_labels=True, init='k-means++',
                    init_size=None, max_iter=100, max_no_improvement=...toff=1, lag_time=1, n_timescales=5, prior_counts=0,
                     reversible_type='mle', verbose=False))])
            Hyperparameter search space:
              featurizer__types         (enum)    choices = (['phi', 'psi'], ['phi', 'psi', 'chi1'])
              cluster__n_clusters       (int)         10 <= x <= 100
        
            ----------------------------------------------------------------------
            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``.
        
        Installation
        ------------
        
        ::
        
            # grab the latest version from github
            $ pip install git+git://github.com/rmcgibbo/osprey.git
        
        ::
        
            # or clone the repo yourself and run `setup.py`
            $ git clone https://github.com/rmcgibbo/osprey.git
            $ cd osprey && python setup.py install
        
        Dependencies
        ------------
        
        -  ``six``
        -  ``pyyaml``
        -  ``numpy``
        -  ``scikit-learn``
        -  ``sqlalchemy``
        -  ``hyperopt`` (recommended, required for ``engine=hyperopt_tpe``)
        -  ``scipy`` (optional, for testing)
        -  ``nose`` (optional, for testing)
        
        On python2.6, the ``argparse`` and ``importlib`` backports are also
        required
        
        .. |Build Status| image:: https://travis-ci.org/rmcgibbo/osprey.svg?branch=master
           :target: https://travis-ci.org/rmcgibbo/osprey
        .. |PyPi version| image:: https://pypip.in/v/osprey/badge.png
           :target: https://pypi.python.org/pypi/osprey/
        .. |Supported Python versions| image:: https://pypip.in/py_versions/osprey/badge.svg
           :target: https://pypi.python.org/pypi/osprey/
        .. |License| image:: https://pypip.in/license/osprey/badge.svg
           :target: https://pypi.python.org/pypi/osprey/
        
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 :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
