fit.runner¶

Module for running parameter optimizations.

The optimization runs either serial or in parallel. The parallel optimization uses multiprocessing, i.e., the runner starts one worker process per core and every worker runs a part of the repeats of the problem.

The OptimizationProblem is pickled and sent to the workers, so it must be picklable: it is initialized in the worker, not before. The workers report every finished run through a queue, which drives the progress display of the runner.

The runner only optimizes. Its result carries the fitted parameters and the settings of the fit, and sbmlsim.fit.report.FitReport turns them into figures and reports, see sbmlsim.fit.parameters.

resolve_n_cores ¶

resolve_n_cores(n_cores)

Resolve the number of worker processes.

Parameters:

Name	Type	Description	Default
`n_cores`	`int \| None`	requested number of workers, `None` uses all available cores but one.	required

Returns:

Type	Description
`int`	Number of workers, at least one and at most the number of available cores.

optimization_progress ¶

optimization_progress(description, size, enabled=True)

Show the progress of the optimization runs on the console.

Parameters:

Name	Type	Description	Default
`description`	`str`	text in front of the progress bar.	required
`size`	`int`	total number of optimization runs.	required
`enabled`	`bool`	show the progress, a plain context without display if `False`.	`True`

Yields:

Type	Description
`Progress \| None`	The progress with a single task, or `None` if it is disabled.

run_optimization ¶

run_optimization(
    problem,
    settings=None,
    size=5,
    algorithm=LEAST_SQUARE,
    seed=None,
    n_cores=1,
    serial=False,
    show_progress=True,
    **kwargs,
)

Run the optimization of the problem.

The runner executes the given OptimizationProblem size times, every repeat starting from its own sample of the parameters, and returns the OptimizationResult with the fitted parameters and the settings of the fit.

Parameters:

Name	Type	Description	Default
`problem`	`OptimizationProblem`	uninitialized problem to optimize (picklable).	required
`settings`	`FitSettings \| None`	settings of the fit, the defaults of `FitSettings` are used if none are given.	`None`
`size`	`int`	number of optimizations.	`5`
`algorithm`	`OptimizationAlgorithmType`	optimization algorithm to use.	`LEAST_SQUARE`
`seed`	`int \| None`	random seed (for sampling of the start values).	`None`
`n_cores`	`int \| None`	number of workers, `None` uses all available cores but one.	`1`
`serial`	`bool`	run the optimization in a serial fashion (debugging).	`False`
`show_progress`	`bool`	show the progress of the runs on the console.	`True`
`kwargs`	`Any`	additional arguments for the optimizer, e.g. xtol.	`{}`

Returns:

Type	Description
`OptimizationResult`	OptimizationResult with the fits of all repeats.

Raises:

Type	Description
`ValueError`	for the removed parameters `fitting_type` and `weighting_local`.

worker ¶

worker(kwargs)

Run a part of the optimizations in a worker process.

Every worker initializes the same problem and would report the same messages about the data, once per core. Only errors of a worker are shown, the runner reports the problem itself.