Skip to main content

|Build Status| |PyPi version| |Supported Python versions| |License| |Documentation Status|

Project description

Osprey
======

|Build Status| |PyPi version| |Supported Python versions| |License|
|Documentation Status|

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.

.. raw:: html

<p align="center">


Full documentation

.. raw:: html

</p>

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/pandegroup/osprey.git

::

# or clone the repo yourself and run `setup.py`
$ git clone https://github.com/pandegroup/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/pandegroup/osprey.svg?branch=master
:target: https://travis-ci.org/pandegroup/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/
.. |Documentation Status| image:: https://readthedocs.org/projects/osprey/badge/?version=latest
:target: http://osprey.rtfd.org

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

osprey-0.4.tar.gz (42.3 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page