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.
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Surrogate Modeling:
- train fast metamodels of computationally expensive problems
- perform surrogate-assisted model reduction for large-scale models/simulators (e.g., biochemical reaction networks)
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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
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Optimization:
- optimize a specified objective function or surrogate model using a variety of approaches
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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
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Version 0.4
Please see the documentation for instructions to install and examples. The easiest way to start using Sciope is through the StochSS online platform (https://app.stochss.org).
- 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!
- Prashant Singh (prashant.singh@it.uu.se)
- Fredrik Wrede (fredrik.wrede@it.uu.se)
- Andreas Hellander (andreas.hellander@it.uu.se)
To cite Sciope, please reference the Bioinformatics application note. Sample Bibtex is given below:
@article{sciope,
author = {Singh, Prashant and Wrede, Fredrik and Hellander, Andreas},
title = "{Scalable machine learning-assisted model exploration and inference using Sciope}",
journal = {Bioinformatics},
year = {2020},
month = {07},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa673},
url = {https://doi.org/10.1093/bioinformatics/btaa673},
note = {btaa673},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa673/33529616/btaa673.pdf},
}