randopt is a Python package for machine learning experiment management, hyper-parameter optimization, and results visualization. Some of its features include:
- result logging and management,
- human-readable format,
- support for parallelism / distributed / asynchronous experiments,
- command-line and programmatic API,
- shareable, flexible Web visualization,
- automatic hyper-parameter search, and
- pure Python - no dependencies !
Installation
pip install randoptUsage
import randopt as ro
def loss(x):
return x**2
e = ro.Experiment('myexp', {
'alpha': ro.Gaussian(mean=0.0, std=1.0, dtype='float'),
})
# Sampling parameters
for i in xrange(100):
e.sample('alpha')
res = loss(e.alpha)
print('Result: ', res)
e.add_result(res)
# Manually setting parameters
e.alpha = 0.00001
res = loss(e.alpha)
e.add_result(res)
# Search over all experiments results, including ones from previous runs
opt = e.minimum()
print('Best result: ', opt.result, ' with params: ', opt.params)Results Visualization
Once you obtained some results, run roviz.py path/to/experiment/folder to visualize them in your web browser.
For more info on visualization and roviz.py, refer to the Visualizing Results tutorial.
Hyper-Parameter Optimization
To generate results and search for good hyper-parameters you can either user ropt.py or write your own optimizaiton script using the Evolutionary and GridSearch classes.
For more info on hyper-parameter optimization, refer to the Optimizing Hyperparams tutorial.
Documentation
For more examples, tutorials, and documentation refer to the wiki.
Contributing
To contribute to Randopt, it is recommended to follow the contribution guidelines.
Acknowledgements
Randopt is maintained by Séb Arnold, with numerous contributions from the following persons.
- Noel Trivedi
- Cyrus Jia
- Daler Asrorov
License
Randopt is released under the Apache 2 License. For more information, refer to the LICENSE file.
I would love to hear how your use Randopt. Feel free to drop me a line !