Scalable inference, optimization and parameter exploration (sciope) is a Python 3 package for performing model-assisted inference and model exploration by large-scale parameter sweeps.
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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 surrogate modeling or Bayesian optimization
- 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 0.2
- pip install . --process-dependency-links
- Configuration
- Dependencies scikit-learn, SciPy, numpy, gpflowopt, ipywidgets, tsfresh, pandas and dask
- How to run tests test suite coming up
- Writing tests Ongoing
- Code review ToDo
- Other guidelines ToDo
- Prashant Singh (prashant.singh@it.uu.se)
- Fredrik Wrede (fredrik.wrede@it.uu.se)
- Andreas Hellander (andreas.hellander@it.uu.se)