pypesto.select.postprocessors

Process a model selection ModelProblem after calibration.

Functions

model_id_binary_postprocessor(problem)	Change a PEtab Select model ID to a binary string.
multi_postprocessor(problem[, postprocessors])	Combine multiple postprocessors into a single postprocessor.
report_postprocessor(problem, output_filepath)	Create a TSV table of model selection results.
save_postprocessor(problem[, output_path, ...])	Save the parameter estimation result.
waterfall_plot_postprocessor(problem[, ...])	Produce a waterfall plot.

pypesto.select.postprocessors.model_id_binary_postprocessor(problem)

Change a PEtab Select model ID to a binary string.

Changes the model ID in-place to be a string like M_ijk, where i, j, k, etc. are 1 if the parameter in that position is estimated, or 0 if the parameter is fixed.

To ensure that other postprocessors (e.g. report_postprocessor()) use this new model ID, when in use with a multi_postprocessor(), ensure this is before the other postprocessors in the postprocessors argument of multi_postprocessor().

Parameters:

problem (ModelProblem) – A model selection ModelProblem that has been optimized.

pypesto.select.postprocessors.multi_postprocessor(problem, postprocessors=None)

Combine multiple postprocessors into a single postprocessor.

See save_postprocessor() for usage hints.

Parameters:

• problem (ModelProblem) – A model selection ModelProblem that has been optimized.

• postprocessors (list[Callable[[ModelProblem], None]]) – A list of postprocessors, which will be sequentially applied to the optimized model problem. The location where results will be stored.

pypesto.select.postprocessors.report_postprocessor(problem, output_filepath, criteria=None)

Create a TSV table of model selection results.

Parameters:

• problem (ModelProblem) – A model selection ModelProblem that has been optimized.

• output_filepath (Union[str, Path]) – The file path where the report will be saved.

• criteria (list[Criterion]) – The criteria that will be in the report. Defaults to nllh, AIC, AICc, and BIC.

pypesto.select.postprocessors.save_postprocessor(problem, output_path='.', use_model_hash=False)

Save the parameter estimation result.

When used, first set the output folder for results, e.g. with functools.partial(). This is because postprocessors should take only a single parameter: an optimized model.

from functools import partial
output_path = 'results'
pp = partial(save_postprocessor, output_path=output_path)
selector = pypesto.select.ModelSelector(
    problem=problem,
    model_postprocessor=pp,
)

Parameters:

• problem (ModelProblem) – A model selection ModelProblem that has been optimized.

• output_path (Union[str, Path]) – The location where output will be stored.

• use_model_hash (bool) – Whether the filename should use the model hash. Defaults to False, in which case the model ID is used instead.

pypesto.select.postprocessors.waterfall_plot_postprocessor(problem, output_path='.')

Produce a waterfall plot.

See save_postprocessor() for usage hints and argument documentation.

Parameters:

• problem (ModelProblem)

• output_path (str | Path)