hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
Science Score: 36.0%
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
-
○Academic publication links
-
✓Committers with academic emails
7 of 104 committers (6.7%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (15.9%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Distributed Asynchronous Hyperparameter Optimization in Python
Basic Info
- Host: GitHub
- Owner: hyperopt
- License: other
- Language: Python
- Default Branch: master
- Homepage: http://hyperopt.github.io/hyperopt
- Size: 5.91 MB
Statistics
- Stars: 7,417
- Watchers: 118
- Forks: 1,066
- Open Issues: 43
- Releases: 0
Topics
Metadata Files
README.md
Hyperopt: Distributed Hyperparameter Optimization
Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.
Getting started
Install hyperopt from PyPI
bash
pip install hyperopt
to run your first example
```python
define an objective function
def objective(args): case, val = args if case == 'case 1': return val else: return val ** 2
define a search space
from hyperopt import hp space = hp.choice('a', [ ('case 1', 1 + hp.lognormal('c1', 0, 1)), ('case 2', hp.uniform('c2', -10, 10)) ])
minimize the objective over the space
from hyperopt import fmin, tpe, spaceeval best = fmin(objective, space, algo=tpe.suggest, maxevals=100)
print(best)
-> {'a': 1, 'c2': 0.01420615366247227}
print(space_eval(space, best))
-> ('case 2', 0.01420615366247227}
```
Contributing
If you're a developer and wish to contribute, please follow these steps.
Setup (based on this)
Create an account on GitHub if you do not already have one.
Fork the project repository: click on the ‘Fork’ button near the top of the page. This creates a copy of the code under your account on the GitHub user account. For more details on how to fork a repository see this guide.
Clone your fork of the hyperopt repo from your GitHub account to your local disk:
bash
git clone https://github.com/<github username>/hyperopt.git
cd hyperopt
Create environment with:
$ python3 -m venv my_envor$ python -m venv my_envor with conda:
$ conda create -n my_env python=3Activate the environment:
$ source my_env/bin/activate
or with conda:
$ conda activate my_envInstall dependencies for extras (you'll need these to run pytest): Linux/UNIX:
$ pip install -e '.[MongoTrials, SparkTrials, ATPE, dev]'
or Windows:
cmd
pip install -e .[MongoTrials]
pip install -e .[SparkTrials]
pip install -e .[ATPE]
pip install -e .[dev]
Add the upstream remote. This saves a reference to the main hyperopt repository, which you can use to keep your repository synchronized with the latest changes:
$ git remote add upstream https://github.com/hyperopt/hyperopt.gitYou should now have a working installation of hyperopt, and your git repository properly configured. The next steps now describe the process of modifying code and submitting a PR:
Synchronize your master branch with the upstream master branch:
bash git checkout master git pull upstream masterCreate a feature branch to hold your development changes:
$ git checkout -b my_featureand start making changes. Always use a feature branch. It’s good practice to never work on the master branch!
We recommend to use Black to format your code before submitting a PR which is installed automatically in step 6.
Then, once you commit ensure that git hooks are activated (Pycharm for example has the option to omit them). This can be done using pre-commit, which is installed automatically in step 6, as follows:
bash pre-commit installThis will run black automatically when you commit on all files you modified, failing if there are any files requiring to be blacked. In case black does not run execute the following:
bash black {source_file_or_directory}Develop the feature on your feature branch on your computer, using Git to do the version control. When you’re done editing, add changed files using git add and then git commit:
bash git add modified_files git commit -m "my first hyperopt commit"The tests for this project use PyTest and can be run by calling
pytest.Record your changes in Git, then push the changes to your GitHub account with:
bash git push -u origin my_feature
Note that dev dependencies require python 3.6+.
Algorithms
Currently three algorithms are implemented in hyperopt:
Hyperopt has been designed to accommodate Bayesian optimization algorithms based on Gaussian processes and regression trees, but these are not currently implemented.
All algorithms can be parallelized in two ways, using:
Documentation
Hyperopt documentation can be found here, but is partly still hosted on the wiki. Here are some quick links to the most relevant pages:
Related Projects
Examples
See projects using hyperopt on the wiki.
Announcements mailing list
Discussion mailing list
Cite
If you use this software for research, please cite the paper (http://proceedings.mlr.press/v28/bergstra13.pdf) as follows:
Bergstra, J., Yamins, D., Cox, D. D. (2013) Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. TProc. of the 30th International Conference on Machine Learning (ICML 2013), June 2013, pp. I-115 to I-23.
Thanks
This project has received support from
- National Science Foundation (IIS-0963668),
- Banting Postdoctoral Fellowship program,
- National Science and Engineering Research Council of Canada (NSERC),
- D-Wave Systems, Inc.
Owner
- Name: hyperopt
- Login: hyperopt
- Kind: organization
- Repositories: 9
- Profile: https://github.com/hyperopt
GitHub Events
Total
- Commit comment event: 1
- Issues event: 138
- Watch event: 236
- Issue comment event: 121
- Push event: 6
- Pull request event: 6
- Fork event: 25
Last Year
- Commit comment event: 1
- Issues event: 138
- Watch event: 236
- Issue comment event: 121
- Push event: 6
- Pull request event: 6
- Fork event: 25
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| James Bergstra | j****a@g****m | 604 |
| Dan Yamins | d****s@g****m | 110 |
| Max Pumperla | m****a@g****m | 84 |
| marload | r****8@g****m | 44 |
| WeichenXu | w****u@d****m | 17 |
| David Warde-Farley | w****r@i****a | 11 |
| Hristijan Bogoevski | h****i@g****m | 11 |
| Aarni Koskela | a****x@i****i | 8 |
| Marc Torrellas | m****o@g****m | 7 |
| Lu Wang | 3****l | 6 |
| Christian Clauss | c****s@m****m | 6 |
| Hannes Schulz | s****z@a****e | 6 |
| Liang Zhang | l****g@d****m | 6 |
| Robert Parini | r****i@g****m | 5 |
| Brent Komer | b****r@g****m | 5 |
| lx | l****b@g****m | 4 |
| Magda Stenius | s****y@g****m | 4 |
| mandjevant | 3****t | 4 |
| Thomas Wiecki | t****i@g****m | 4 |
| Quentin | q****d@g****m | 4 |
| Michael Mior | m****r@m****u | 4 |
| Will Groves | w****l@g****m | 3 |
| pre-commit-ci[bot] | 6****] | 3 |
| Jake Bian | j****e@k****o | 3 |
| Philip May | e****o@g****m | 3 |
| LeowWB | 4****B | 3 |
| Harish Rajagopal | h****s@g****m | 3 |
| Andrej Palička | a****a@g****m | 2 |
| Nicolas Pinto | p****o@a****u | 2 |
| Sam Hiatt | s****t@g****m | 2 |
| and 74 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 432
- Total pull requests: 43
- Average time to close issues: about 5 years
- Average time to close pull requests: 7 months
- Total issue authors: 329
- Total pull request authors: 31
- Average comments per issue: 3.33
- Average comments per pull request: 1.7
- Merged pull requests: 26
- Bot issues: 0
- Bot pull requests: 6
Past Year
- Issues: 9
- Pull requests: 6
- Average time to close issues: 4 months
- Average time to close pull requests: 2 months
- Issue authors: 9
- Pull request authors: 5
- Average comments per issue: 0.78
- Average comments per pull request: 0.17
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 2
Top Authors
Issue Authors
- jaberg (44)
- yamins81 (6)
- dwf (5)
- Arktius (4)
- PhilipMay (4)
- ghost (3)
- shaddyab (3)
- david-waterworth (3)
- NormanTUD (3)
- guillaume-chevalier (3)
- ernestum (3)
- dwiel (2)
- JadBatmobile (2)
- bkozyrskiy (2)
- bacalfa (2)
Pull Request Authors
- pre-commit-ci[bot] (7)
- akx (4)
- genixpro (3)
- rharish101 (3)
- MorrisNein (2)
- freekboutkan-ah (2)
- FrankwaP (2)
- stevetracvc (2)
- bluestealth (2)
- nikhilgupta0 (2)
- NickHilton (2)
- zzz4zzz (2)
- WeichenXu123 (2)
- andriyor (1)
- marctorsoc (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 12
-
Total downloads:
- pypi 2,858,643 last-month
- Total docker downloads: 24,566,934
-
Total dependent packages: 147
(may contain duplicates) -
Total dependent repositories: 2,125
(may contain duplicates) - Total versions: 65
- Total maintainers: 4
pypi.org: hyperopt
Distributed Asynchronous Hyperparameter Optimization
- Homepage: https://hyperopt.github.io/hyperopt
- Documentation: https://hyperopt.readthedocs.io/
- License: BSD
-
Latest release: 0.2.7
published over 4 years ago
Rankings
Maintainers (2)
alpine-v3.18: py3-hyperopt-pyc
Precompiled Python bytecode for py3-hyperopt
- Homepage: https://hyperopt.github.io/hyperopt/
- License: BSD-3-Clause
-
Latest release: 0.2.7-r2
published about 3 years ago
Rankings
Maintainers (1)
alpine-v3.18: py3-hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
- Homepage: https://hyperopt.github.io/hyperopt/
- License: BSD-3-Clause
-
Latest release: 0.2.7-r2
published about 3 years ago
Rankings
Maintainers (1)
alpine-edge: py3-hyperopt-pyc
Precompiled Python bytecode for py3-hyperopt
- Homepage: https://hyperopt.github.io/hyperopt/
- License: BSD-3-Clause
- Status: removed
-
Latest release: 0.2.7-r2
published about 3 years ago
Rankings
Maintainers (1)
alpine-edge: py3-hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
- Homepage: https://hyperopt.github.io/hyperopt/
- License: BSD-3-Clause
- Status: removed
-
Latest release: 0.2.7-r2
published about 3 years ago
Rankings
Maintainers (1)
alpine-v3.13: py3-hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
- Homepage: https://hyperopt.github.io/hyperopt/
- License: BSD-3-Clause
-
Latest release: 0.2.5-r1
published over 5 years ago
Rankings
Maintainers (1)
conda-forge.org: hyperopt
Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.
- Homepage: https://github.com/hyperopt/hyperopt
- License: BSD-3-Clause
-
Latest release: 0.2.7
published about 4 years ago
Rankings
alpine-v3.14: py3-hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
- Homepage: https://hyperopt.github.io/hyperopt/
- License: BSD-3-Clause
-
Latest release: 0.2.5-r2
published about 5 years ago
Rankings
Maintainers (1)
alpine-v3.15: py3-hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
- Homepage: https://hyperopt.github.io/hyperopt/
- License: BSD-3-Clause
-
Latest release: 0.2.5-r2
published about 5 years ago
Rankings
Maintainers (1)
alpine-v3.16: py3-hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
- Homepage: https://hyperopt.github.io/hyperopt/
- License: BSD-3-Clause
-
Latest release: 0.2.5-r3
published about 4 years ago
Rankings
Maintainers (1)
alpine-v3.17: py3-hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
- Homepage: https://hyperopt.github.io/hyperopt/
- License: BSD-3-Clause
-
Latest release: 0.2.7-r0
published almost 4 years ago
Rankings
Maintainers (1)
pypi.org: quantybt
lightweight backtesting framework with in-built advanced statistical backtesting methods and custom models
- Documentation: https://quantybt.readthedocs.io/
- License: MIT
- Status: removed
-
Latest release: 0.1.11
published about 1 year ago
Rankings
Maintainers (1)
Dependencies
- cloudpickle *
- future *
- networkx >=2.2
- numpy >=1.17
- py4j *
- scipy *
- six *
- tqdm *
- actions/cache v2 composite
- actions/checkout v2 composite
- actions/setup-python v2 composite