README

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.

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 surrogate modeling or Bayesian optimization
  • 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?

• 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

Contribution guidelines

• Writing tests Ongoing
• Code review ToDo
• Other guidelines ToDo

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)