sbmlsim.fit.optimization

Optimization of parameter fitting problem.

Module Contents

Classes

RuntimeErrorOptimizeResult	Error in opimization.
OptimizationProblem	Parameter optimization problem.

Attributes

logger

sbmlsim.fit.optimization.logger

class sbmlsim.fit.optimization.RuntimeErrorOptimizeResult

Error in opimization.

status: str = '-1'

success: bool = False

duration: float

cost: float

optimality: float

class sbmlsim.fit.optimization.OptimizationProblem(opid, fit_experiments, fit_parameters, base_path=None, data_path=None)

Bases: sbmlsim.serialization.ObjectJSONEncoder

Parameter optimization problem.

Parameters:

• opid (str) –

• fit_experiments (List[sbmlsim.fit.objects.FitExperiment]) –

• fit_parameters (List[sbmlsim.fit.objects.FitParameter]) –

• base_path (pathlib.Path) –

• data_path (pathlib.Path) –

__repr__()

Get representation.

Return type:

str

__str__()

Get string representation.

This can be run before initialization.

Return type:

str

to_dict()

Convert to dictionary.

Return type:

Dict[str, Any]

to_json(path=None)

Store OptimizationResult as json.

Uses the to_dict method.

Parameters:

path (Optional[pathlib.Path]) –

Return type:

Union[str, pathlib.Path]

report(path=None, print_output=True)

Print and write report.

Can only be called after initialization.

Parameters:

• path (pathlib.Path) –

• print_output (bool) –

Return type:

str

initialize(residual, loss_function, weighting_curves, weighting_points, variable_step_size=True, relative_tolerance=1e-06, absolute_tolerance=1e-06)

Initialize Optimization problem.

Performs precalculations, resolving data, calculating weights. Creates and attaches simulator for the given problem.

Parameters:

• residual (Optional[sbmlsim.fit.options.ResidualType]) – handling of residuals

• loss_function (sbmlsim.fit.options.LossFunctionType) – loss function for residual transformation

• weighting_curves (List[sbmlsim.fit.options.WeightingCurvesType]) – list of options for weighting curves (fit mappings)

• weighting_points (Optional[sbmlsim.fit.options.WeightingPointsType]) – weighting of points

• absolute_tolerance (float) – absolute tolerance of simulator

• relative_tolerance (float) – relative tolerance of simulator

• variable_step_size (bool) – use variable step size in solver

Return type:

None

set_simulator(simulator)

Set the simulator on the runner and the experiments.

Parameters:

simulator –

Returns:

optimize(size=5, algorithm=OptimizationAlgorithmType.LEAST_SQUARE, sampling=SamplingType.UNIFORM, seed=None, **kwargs)

Run parameter optimization.

To change the weighting or handling of residuals reinitialize the optimization algorithm.

Parameters:

• size (Optional[int]) –

• algorithm (sbmlsim.fit.options.OptimizationAlgorithmType) –

• sampling (sbmlsim.fit.sampling.SamplingType) –

• seed (Optional[int]) –

Return type:

Tuple[List[scipy.optimize.OptimizeResult], List]

_optimize_single(x0=None, algorithm=OptimizationAlgorithmType.LEAST_SQUARE, **kwargs)

Run single optimization with x0 start values.

Parameters:

• x0 (numpy.ndarray) – parameter start vector (important for deterministic optimizers)

• algorithm – optimization algorithm and method

• kwargs –

Returns:

Return type:

Tuple[scipy.optimize.OptimizeResult, List]

cost_least_square(xlog)

Get least square costs for parameters.

Parameters:

xlog (numpy.ndarray) –

Return type:

float

residuals(xlog, complete_data=False)

Calculate residuals for given parameter vector.

Optimization is performed in logarithmic parameter space to account for xtol in largely varying parameters. see https://github.com/scipy/scipy/issues/7632

Parameters:

• xlog (numpy.ndarray) – logarithmic parameter vector

• complete_data – boolean flag to return additional information

Returns:

vector of weighted residuals