skearnes / osprey

osprey is the plumbing for machine learning hyperparameter optimization.

Osprey

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.

Full documentation

Example (with 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