sbmlsim.fit.analysis
¶
Analysis of fitting results.
Module Contents¶
Classes¶
Class for analyzing optimization results. |
Attributes¶
- 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)[source]¶
Class for analyzing optimization results.
Creates all plots and results.
- Parameters
opt_result (sbmlsim.fit.result.OptimizationResult) –
output_name (str) –
output_dir (pathlib.Path) –
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(self, mpl_parameters=None)[source]¶
Execute complete analysis.
This creates all plots and reports.
- Parameters
mpl_parameters (Dict[str, Any]) –
- Return type
None
- _create_mpl_figure(self, width=5.0, height=5.0)[source]¶
Create matplotlib figure.
- Parameters
width (float) –
height (float) –
- Return type
Tuple[matplotlib.figure.Figure, matplotlib.figure.Axes]
- _save_mpl_figure(self, fig, path)[source]¶
Save matplotlib figure to path.
- Parameters
path (pathlib.Path) –
- Return type
None
- plot_fit(self, output_dir, x)[source]¶
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(self, output_dir, x)[source]¶
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(self, x)[source]¶
Calculate cost dataframe for given parameter set.
- Parameters
x (numpy.ndarray) –
- Return type
pandas.DataFrame
- _datapoints_df(self, x)[source]¶
Calculate data point dataframe for given parameter set.
- Parameters
x (numpy.ndarray) –
- Return type
pandas.DataFrame
- plot_datapoint_scatter(self, x, path)[source]¶
Plot cost scatter plot.
Compares cost of model parameters to the given parameter set.
- Parameters
x (numpy.ndarray) –
path (pathlib.Path) –
- plot_residual_scatter(self, x, path)[source]¶
Plot residual plot.
- Parameters
x (numpy.ndarray) –
path (pathlib.Path) –
- plot_cost_bar(self, x, path)[source]¶
Plot cost bar plot.
Compare costs of all curves.
- Parameters
x (numpy.ndarray) –
path (pathlib.Path) –
- Return type
None
- plot_residual_boxplot(self, x, path)[source]¶
Plot residual boxplot.
Compare costs of all curves.
- Parameters
x (numpy.ndarray) –
path (pathlib.Path) –
- Return type
None
- plot_cost_scatter(self, x, path)[source]¶
Plot cost scatter plot.
Compares cost of model parameters to the given parameter set.
- Parameters
x (numpy.ndarray) –
path (pathlib.Path) –
- plot_waterfall(self, path)[source]¶
Create waterfall plot for the fit results.
Plots the optimization runs sorted by cost.
- Parameters
path (pathlib.Path) –