Optimization results (biomass.results)

class biomass.result.OptimizationResults(model)
__post_init__()

Create optimization_results/ in the model folder.

Return type:

None

dynamic_assessment(include_original=False)

Compute objective values using estimated parameters.

Parameters:

include_original (bool (default: False)) – If True, an objective value simulated with original parameters will also be shown.

Return type:

None

Examples

>>> from biomass import create_model, OptimizationResults
>>> from biomass.models import copy_to_current
>>> copy_to_current("Nakakuki_Cell_2010")
>>> model = create_model("Nakakuki_Cell_2010")
>>> res = OptimizationResults(model)
>>> res.dynamic_assessment()

Notes

Output:

  • optimization_results/fitness_assessment.csv

savefig(*, figsize=None, config=None, boxplot_kws=None)

Visualize estimated parameter sets using seaborn.boxplot.

Parameters:

  • figsize (Tuple[float, float], optional) – Width, height in inches.

  • config (dict, optional) – A dictionary object for setting matplotlib.rcParams.

  • boxplot_kws (dict, optional) – Keyword arguments to pass to seaborn.boxplot.

Return type:

None

Examples

>>> from biomass import create_model, OptimizationResults
>>> from biomass.models import copy_to_current
>>> copy_to_current("Nakakuki_Cell_2010")
>>> model = create_model("Nakakuki_Cell_2010")
>>> res = OptimizationResults(model)
>>> res.savefig(figsize=(16,5), boxplot_kws={"orient": "v"})

Notes

Output:

  • optimization_results/estimated_parameter_sets.pdf

to_csv()

Save optimized parameters as CSV file format.

Return type:

None

Examples

>>> from biomass import create_model, OptimizationResults
>>> from biomass.models import copy_to_current
>>> copy_to_current("Nakakuki_Cell_2010")
>>> model = create_model("Nakakuki_Cell_2010")
>>> res = OptimizationResults(model)
>>> res.to_csv()

Notes

Output:

  • optimization_results/optimized_params.csv

  • optimization_results/optimized_initials.csv

trace_obj(*, config=None, xlabel='Iteration', ylabel='Objective function value', xticks=None, yticks=None, message_head='differential_evolution step', sep=':', prefix='=')

Visualize objective function traces for different optimization runs.

Parameters:

  • config (dict, optional) – A dictionary object for setting matplotlib.rcParams.

  • xlabel (str (default: "Iteration")) – The label for the x-axis.

  • ylabel (str (default: "Objective function value")) – The label for the x-axis.

  • xticks (list, optional) – The list of xtick locations.

  • yticks (list, optional) – The list of ytick locations.

  • message_head (str (default: "differential_evolution step")) – Beginning of the progress status message.

  • sep (str (default: ":")) – Suffix for iteration number.

  • prefix (str (default: "=")) – Prefix for objective function value.

Return type:

None

Examples

>>> from biomass import create_model, OptimizationResults
>>> from biomass.models import copy_to_current
>>> copy_to_current("Nakakuki_Cell_2010")
>>> model = create_model("Nakakuki_Cell_2010")
>>> res = OptimizationResults(model)
>>> res.trace_obj()

Notes

Output:

  • optimization_results/obj_func_traces.pdf