sbmlsim.fit.analysis

Analysis of fitting results.

Module Contents

Classes

OptimizationAnalysis

Class for analyzing optimization results.

Attributes

logger

sbmlsim.fit.analysis.logger

class sbmlsim.fit.analysis.OptimizationAnalysis(opt_result, output_name, output_dir, op=None, show_plots=True, show_titles=True, residual=None, loss_function=None, weighting_curves=None, weighting_points=None, variable_step_size=True, absolute_tolerance=1e-06, relative_tolerance=1e-06, image_format='svg', **kwargs)

Class for analyzing optimization results.

Creates all plots and results.

Parameters:

• opt_result (sbmlsim.fit.result.OptimizationResult) –

• output_name (str) –

• output_dir (pathlib.Path) –

• op (sbmlsim.fit.optimization.OptimizationProblem) –

• show_plots (bool) –

• show_titles (bool) –

• residual (sbmlsim.fit.options.ResidualType) –

• loss_function (sbmlsim.fit.options.LossFunctionType) –

• weighting_curves (List[sbmlsim.fit.options.WeightingCurvesType]) –

• weighting_points (sbmlsim.fit.options.WeightingPointsType) –

• variable_step_size (bool) –

• absolute_tolerance (float) –

• relative_tolerance (float) –

• image_format (str) –

run(mpl_parameters=None)

Execute complete analysis.

This creates all plots and reports.

Parameters:

mpl_parameters (Dict[str, Any]) –

Return type:

None

html_report(path)

Create HTML report of the fit.

Parameters:

path (pathlib.Path) –

_create_mpl_figure(width=5.0, height=5.0)

Create matplotlib figure.

Parameters:

• width (float) –

• height (float) –

Return type:

Tuple[matplotlib.figure.Figure, matplotlib.figure.Axes]

_save_mpl_figure(fig, path)

Save matplotlib figure to path.

Parameters:

path (pathlib.Path) –

Return type:

None

plot_fit(output_dir, x)

Plot fitted curves with experimental data for given parameter set x.

Creates an overview of all fit mappings.

Parameters:

• output_dir (pathlib.Path) – path to figures

• x (numpy.ndarray) – parameters to evaluate

Returns:

None

Return type:

None

plot_fit_residual(output_dir, x)

Plot resulting fit for all individual fit mappings.

This consists of - data - prediction - residuals - weighed residuals squared

For better analysis log and linear results are depicted. :param x: parameters to evaluate

Parameters:

• output_dir (pathlib.Path) –

• x (numpy.ndarray) –

Return type:

None

_cost_df(x)

Calculate cost dataframe for given parameter set.

Parameters:

x (numpy.ndarray) –

Return type:

pandas.DataFrame

_datapoints_df(x)

Calculate data point dataframe for given parameter set.

Parameters:

x (numpy.ndarray) –

Return type:

pandas.DataFrame

plot_datapoint_scatter(x, path)

Plot cost scatter plot.

Compares cost of model parameters to the given parameter set.

Parameters:

• x (numpy.ndarray) –

• path (pathlib.Path) –

plot_residual_scatter(x, path)

Plot residual plot.

Parameters:

• x (numpy.ndarray) –

• path (pathlib.Path) –

plot_cost_bar(x, path)

Plot cost bar plot.

Compare costs of all curves.

Parameters:

• x (numpy.ndarray) –

• path (pathlib.Path) –

Return type:

None

plot_residual_boxplot(x, path)

Plot residual boxplot.

Compare costs of all curves.

Parameters:

• x (numpy.ndarray) –

• path (pathlib.Path) –

Return type:

None

plot_cost_scatter(x, path)

Plot cost scatter plot.

Compares cost of model parameters to the given parameter set.

Parameters:

• x (numpy.ndarray) –

• path (pathlib.Path) –

plot_waterfall(path)

Create waterfall plot for the fit results.

Plots the optimization runs sorted by cost.

Parameters:

path (pathlib.Path) –

plot_traces(path)

Plot optimization traces.

Optimization time course of costs.

Parameters:

path (pathlib.Path) –

Return type:

None

plot_correlation(path)

Plot correlation of parameters for analysis.

Parameters:

path (pathlib.Path) –

Return type:

None