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https://github.com/rmcgibbo/osprey
osprey is the plumbing for machine learning hyperparameter optimization.
https://github.com/rmcgibbo/osprey
Last synced: about 2 months ago
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osprey is the plumbing for machine learning hyperparameter optimization.
- Host: GitHub
- URL: https://github.com/rmcgibbo/osprey
- Owner: rmcgibbo
- License: apache-2.0
- Fork: true (msmbuilder/osprey)
- Created: 2014-12-15T07:35:09.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2015-12-05T00:41:10.000Z (almost 9 years ago)
- Last Synced: 2024-07-10T12:06:33.176Z (3 months ago)
- Language: Python
- Homepage: http://osprey.rtfd.org
- Size: 256 KB
- Stars: 3
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Osprey
======
[](https://travis-ci.org/pandegroup/osprey) [](https://pypi.python.org/pypi/osprey/) [](https://pypi.python.org/pypi/osprey/) [](https://pypi.python.org/pypi/osprey/)
[](http://osprey.rtfd.org)
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 [MSMBuilder](https://github.com/msmbuilder/msmbuilder) models/datasets)
-------------------------------------------------------------
```
$ cat config.yaml
estimator:
eval_scope: msmbuilder
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` simultaneously 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