Osprey

Osprey: Hyperparameter Optimization for Machine Learning - Published in JOSS (2016)

https://github.com/msmbuilder/osprey

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

○
CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 9 DOI reference(s) in README and JOSS metadata
✓
Academic publication links
Links to: joss.theoj.org
✓
Committers with academic emails
7 of 14 committers (50.0%) from academic institutions
○
Institutional organization owner
✓
JOSS paper metadata
Published in Journal of Open Source Software

Keywords

cross-validation hyperparameter-optimization machine-learning models optimization pretty-logo python scikit-learn

Scientific Fields

Artificial Intelligence and Machine Learning Computer Science - 32% confidence

Last synced: 4 months ago · JSON representation

Repository

🦅Hyperparameter optimization for machine learning pipelines 🦅

Basic Info

Host: GitHub
Owner: msmbuilder
License: apache-2.0
Language: Python
Default Branch: master
Homepage: http://msmbuilder.org/osprey
Size: 974 KB

Statistics

Stars: 73
Watchers: 11
Forks: 26
Open Issues: 18
Releases: 3

Topics

cross-validation hyperparameter-optimization machine-learning models optimization pretty-logo python scikit-learn

Created about 11 years ago · Last pushed about 5 years ago

Metadata Files

Readme Contributing License

Osprey

Logo

Osprey is an easy-to-use tool for hyperparameter optimization of 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.

Documentation

For full documentation, please visit the Osprey homepage.

Installation

If you have an Anaconda Python distribution, installation is as easy as: $ conda install -c omnia osprey

You can also install Osprey with pip: $ pip install osprey

Alternatively, 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 install

Example using MSMBuilder

Below is an example of an osprey config file to cross validate Markov state models based on varying the number of clusters and dihedral angles used in a model: ```yaml estimator: evalscope: msmbuilder eval: | Pipeline([ ('featurizer', DihedralFeaturizer(types=['phi', 'psi'])), ('cluster', MiniBatchKMeans()), ('msm', MarkovStateModel(ntimescales=5, verbose=False)), ])

searchspace: clusternclusters: 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

...

Beginning iteration 1 / 1

History contains: 0 trials Choosing next hyperparameters with random... {'clustern_clusters': 20, 'featurizertypes': ['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`.

Dependencies

python>=2.7.11
six>=1.10.0
pyyaml>=3.11
numpy>=1.10.4
scipy>=0.17.0
scikit-learn>=0.17.0
sqlalchemy>=1.0.10
bokeh>=0.12.0
matplotlib>=1.5.0
pandas>=0.18.0
GPy (optional, required for gp strategy)
hyperopt (optional, required for hyperopt_tpe strategy)
nose (optional, for testing)

Contributing

In case you encounter any issues with this package, please consider submitting a ticket to the GitHub Issue Tracker. We also welcome any feature requests and highly encourage users to submit pull requests for bug fixes and improvements.

For more detailed information, please refer to our documentation.

Citing

If you use Osprey in your research, please cite:

bibtex @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: Hyperparameter Optimization for Machine Learning}, month = sep, year = 2016, doi = {10.21105/joss.000341}, url = {http://dx.doi.org/10.21105/joss.00034} }

Owner

Name: MSMBuilder
Login: msmbuilder
Kind: organization
Email: msmbuilder-user@lists.stanford.edu

Website: http://msmbuilder.org
Repositories: 12
Profile: https://github.com/msmbuilder

Statistical models for biomolecular dynamics

JOSS Publication

Osprey: Hyperparameter Optimization for Machine Learning

Published

September 07, 2016

DOI

10.21105/joss.00034

Volume 1, Issue 5, Page 34

Authors

Robert T. McGibbon
Stanford University

Carlos X. Hernández

Stanford University

Matthew P. Harrigan
Stanford University

Steven Kearnes
Stanford University

Mohammad M. Sultan
Stanford University

Stanislaw Jastrzebski
Jagiellonian University

Brooke E. Husic
Stanford University

Vijay S. Pande
Stanford University

Editor

Arfon Smith

Papers & Mentions

Total mentions: 3

Visualization of protein interaction networks: problems and solutions

DOI: 10.1186/1471-2105-14-S1-S1
OpenAlex ID: https://openalex.org/W2016958746
Published: January 2013

Last synced: 2 months ago

Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface

DOI: 10.1371/journal.pcbi.1007447
OpenAlex ID: https://openalex.org/W3033967289
Published: June 2020

Last synced: 2 months ago

A critical analysis of computational protein design with sparse residue interaction graphs

DOI: 10.1371/journal.pcbi.1005346
OpenAlex ID: https://openalex.org/W2602328396
Published: March 2017

Last synced: 2 months ago

GitHub Events

Total

Last Year

Committers

Last synced: 5 months ago

All Time

Total Commits: 531
Total Committers: 14
Avg Commits per committer: 37.929
Development Distribution Score (DDS): 0.682

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Robert McGibbon	r**o@g**m	169
Carlos Hernandez	c**h@s**u	126
Robert Arbon	r**n@g**m	48
Carlos Hernández	c****z	46
Matthew Harrigan	h**n@s**u	45
skearnes	k**s@s**u	33
Unknown	r**n@b**k	31
Mohammad Muneeb Sultan	m**n@g**m	21
Steven Kearnes	s**s@g**m	3
Stanislw Jastrzebski	s**i@g**m	2
Joshua L. Adelman	j**n@g**m	2
Brooke Husic	b**c@s**u	2
Juan Eiros	j**4@i**k	2
bhusic@stanford.edu	b**c@s**u	1

Committer Domains (Top 20 + Academic)

stanford.edu: 4 sherlock-ln01.stanford.edu: 1 imperial.ac.uk: 1 bristol.ac.uk: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 52
Total pull requests: 48
Average time to close issues: 4 months
Average time to close pull requests: about 2 months
Total issue authors: 11
Total pull request authors: 6
Average comments per issue: 2.13
Average comments per pull request: 0.33
Merged pull requests: 42
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

cxhernandez (22)
RobertArbon (7)
synapticarbors (5)
jeiros (5)
brookehus (4)
mpharrigan (4)
msultan (1)
orestxherija (1)
rafwiewiora (1)
rsatijaUT (1)
nhstanley (1)

Pull Request Authors

cxhernandez (31)
RobertArbon (9)
mpharrigan (5)
synapticarbors (1)
brookehus (1)
jeiros (1)

Top Labels

Issue Labels

bug (8) docs (7)

Pull Request Labels

docs (7) bug (4) needs docs (2) needs test (1) tested (1)

Packages

Total packages: 1
Total downloads:
- pypi 63 last-month

Total dependent packages: 0
Total dependent repositories: 2
Total versions: 6
Total maintainers: 2

pypi.org: osprey

Homepage: https://github.com/msmbuilder/osprey
Documentation: https://osprey.readthedocs.io/
License: Apache Software License
Latest release: 1.1.0
published over 9 years ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 2
Downloads: 63 Last month

Rankings

Forks count: 7.6%

Stargazers count: 8.0%

Dependent packages count: 10.1%

Dependent repos count: 11.5%

Average: 11.9%

Downloads: 22.3%

Maintainers (2)

rmcgibbo cxhernandez

Last synced: 4 months ago

Dependencies

docs/requirements.txt pypi

numpydoc =0.7
python *

requirements.txt pypi

bokeh >=0.12.0
matplotlib >=1.5.0
numpy >=1.10.4
pandas >=0.18.0
pyyaml >=3.11
scikit-learn >=0.17.0
scipy >=0.17.0
six >=1.10.0
sqlalchemy >=1.0.10

Osprey

Science Score: 95.0%

Keywords

Scientific Fields

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Osprey

Documentation

Installation

Example using MSMBuilder

Beginning iteration 1 / 1

[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.8s finished

(best score so far = 4.080646)

Dependencies

Contributing

Citing

Owner

JOSS Publication

Osprey: Hyperparameter Optimization for Machine Learning

Authors

Editor

Tags

Papers & Mentions

Visualization of protein interaction networks: problems and solutions

Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface

A critical analysis of computational protein design with sparse residue interaction graphs

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: osprey

Rankings

Maintainers (2)

Dependencies