ionbench 0.4.3

A benchmarking tool for comparing different parameter optimization algorithms for ion channel models

ionBench

A benchmarking tool for comparing different parameter optimization algorithms for ion channel models.

Project Structure

The tree structure of this project is outlined below.

├───docs
├───ionbench
│   ├───benchmarker
│   ├───data
│   │   ├───loewe2016
│   │   ├───moreno2016
│   │   ├───staircase
│   │   └───test
│   ├───modification
│   ├───optimisers
│   │   ├───external_optimisers
│   │   ├───pints_optimisers
│   │   └───scipy_optimisers
│   ├───problems
│   ├───tracker
│   ├───uncertainty
│   └───utils
├───scipts
│   └───figures
└───test

The docs directory contains information and guides on how to use the benchmarker problems and the optimisation algorithms.

The ionbench directory contains the majority of the code, including the benchmarker and problems classes and the different optimisation algorithms. This is what is installed using pip.

• The benchmarker subdirectory contains the main Benchmarker class that the test problems all inherit from and defines the core features of the benchmarkers.

• The data subdirectory is split up into the available benchmark problems. Each subdirectory contains the Myokit .mmt files, the voltage clamp protocols stored as .csv files where relevant, and output data to train the models, also stored as a .csv. It also contains the data for the test problem which is used for testing the benchmarker.

• The modification subdirectory contains the modification classes, generalised settings for handling transformations and bounds.

• The optimisers subdirectory contains all the optimisation algorithms that are currently implemented. These are then further subdivided into three directories, containing the optimisers from pints, from scipy, and other optimisation algorithms used in fitting ion channel models that have been implemented specifically for ionBench.

• The problems subdirectory contains the classes for the available benchmarking problems. This features the problems from Loewe et al. 2016 and Moreno et al. 2016. In addition to these previously defined problems, we have introduced two further problems, a Hodgkin-Huxley IKr model from Beattie et al. 2017 and a Markov IKr model from Fink et al. 2008 using the staircase protocol.

• The tracker subdirectory contains the Tracker class which records the performance metrics over the course of an optimisation.

• The uncertainty subdirectory contains functions for determining uncertainty and unidentifiability in the problems, such as calculating profile likelihood plots and Fisher's Information Matrix.

• The utils subdirectory contains utility functions for the operation of ionBench. This includes code for handling the steady states of the models and a function to initiate multiple runs of the same approach and record the results.

The scripts directory contains scripts for generating figures, tables and data for the paper.

The test directory contains unit tests for ensuring changes do not break previous functionality.

Installation

ionBench can be installed using pip.

pip install ionbench

Note that ionBench uses myokit to do its simulations, which relies on CVODES (from Sundials). For Linux and macOS users a working installation of CVODES is required. For Windows users, CVODES should be automatically installed with myokit.

Getting Started

If you want to use ionBench, check out the introduction and tutorial in the docs directory.

Workflow

The intended workflow for using the benchmarker is to generate a benchmarker object, set up the optimisers modification and apply it to the benchmarker, and pass the benchmarker into the optimisation algorithm to evaluate. All optimisers should accept a single benchmarker as input with all other inputs being optional.

import ionbench
bm = ionbench.problems.staircase.HH()
modification = ionbench.optimisers.pints_optimisers.cmaes_pints.get_modification()
modification.apply(bm)
optimisedParameters = ionbench.optimisers.scipy_optimisers.nelderMead_scipy.run(bm)