The sciope toolbox for surrogate modeling, likelihood-free parameter inference and black-box optimization.
Project description
README
Scalable inference, optimization and parameter exploration (sciope) is a Python 3 package for performing machine learning-assisted inference and model exploration by large-scale parameter sweeps. Please see the documentation for examples.
What can the sciope toolbox do?
-
Surrogate Modeling:
- train fast metamodels of computationally expensive problems
- perform surrogate-assisted model reduction for large-scale models/simulators (e.g., biochemical reaction networks)
-
Inference:
- perform likelihood-free parameter inference using parallel ABC
- train surrogate models (ANNs) as expressive summary statistics for likelihood-free inference
- perform efficient parameter sweeps based on statistical designs and sampling techniques
-
Optimization:
- optimize a specified objective function or surrogate model using a variety of approaches
-
Model exploration:
- perform large distributed parameter sweep applications for any black-box model/simulator which output time series data
- generates time series features/summary statistics on simulation output and visualize parameter points in feature space
- interactive labeling of paramater points in feature space according to the users preferences over the diversity of model behaviors
- supports semi-supervised learning and downstream classifiers
-
Version 0.2
How do I get set up?
Please see the documentation for instructions to install and examples.
Steps to a successful contribution
- Fork Sciope (https://help.github.com/articles/fork-a-repo/)
- Make the changes to the source code in your fork.
- Check your code with PEP8 or pylint. Please limit text to 80 columns wide.
- Each feature or bugfix commit should consist of the corresponding code, tests, and documentation.
- Create a pull request to the develop branch in Sciope.
- Please feel free to use the comments section to communicate with us, and raise issues as appropriate.
- The pull request gets accepted and your new feature will soon be integrated into Sciope!
Who do I talk to?
- Prashant Singh (prashant.singh@it.uu.se)
- Fredrik Wrede (fredrik.wrede@it.uu.se)
- Andreas Hellander (andreas.hellander@it.uu.se)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
sciope-0.2.tar.gz
(40.4 kB
view hashes)
Built Distribution
sciope-0.2-py3-none-any.whl
(69.9 kB
view hashes)