sbmlsim.fit.objects

Definition of Objects used in FitProblems and optimization.

Module Contents

Classes

FitExperiment	A parameter fitting experiment.
FitMapping	Mapping of reference data to observable data.
FitParameter	Parameter adjusted in a parameter optimization.
FitData	Data used in a fit.
FitDataInitialized	Initialized FitData with actual data content.

Attributes

logger

sbmlsim.fit.objects.logger

class sbmlsim.fit.objects.FitExperiment(experiment, mappings=None, weights=None, use_mapping_weights=False, fit_parameters=None)

A parameter fitting experiment.

A parameter fitting experiment consists of multiple mapping (reference data to observable). The individual mappings can be weighted differently in the fitting.

Parameters

• experiment (Callable) –

• mappings (List[str]) –

• weights (Union[float, List[float]]) –

• use_mapping_weights (bool) –

• fit_parameters (Dict[str, List[FitParameter]]) –

property weights(self)

Weights of fit mappings.

Return type

List[float]

static reduce(fit_experiments)

Collect fit mappings of multiple FitExperiments if these can be combined.

Parameters

fit_experiments (Iterable[FitExperiment]) –

Return type

List[FitExperiment]

__repr__(self)

Get representation.

Return type

str

__str__(self)

Get string.

Return type

str

class sbmlsim.fit.objects.FitMapping(experiment, reference, observable, weight=None, metadata=None)

Mapping of reference data to observable data.

In the optimization the difference between the reference data (ground truth) and the observable (predicted data) is minimized. The weight allows to weight the FitMapping.

Parameters

• experiment (Any) –

• reference (FitData) –

• observable (FitData) –

• weight (float) –

• metadata (Any) –

property weight(self)

Return defined weight or count of the reference.

Return type

float

class sbmlsim.fit.objects.FitParameter(pid, start_value=None, lower_bound=- np.Inf, upper_bound=np.Inf, unit=None)

Parameter adjusted in a parameter optimization.

The bounds define the box in which the parameter can be varied. The start value is the initial value in the parameter fitting for algorithms which use it.

Parameters

• pid (str) –

• start_value (float) –

• lower_bound (float) –

• upper_bound (float) –

• unit (str) –

__eq__(self, other)

Check for equality.

Uses math.isclose for all comparisons of numerical values.

Parameters

other (object) –

Return type

bool

__repr__(self)

Get string representation.

Return type

str

to_json(self, path=None)

Serialize to JSON.

Serializes to file if path is provided, otherwise returns JSON string.

Parameters

path (pathlib.Path) –

Return type

Optional[str]

static from_json(json_info)

Load from JSON.

Parameters

json_info (Union[str, pathlib.Path]) –

Return type

FitParameter

class sbmlsim.fit.objects.FitData(experiment, xid, yid, xid_sd=None, xid_se=None, yid_sd=None, yid_se=None, count=None, dataset=None, task=None, function=None)

Data used in a fit.

This is either data from a dataset, a simulation results from a task or functional data, i.e. calculated from other data.

Parameters

• experiment (Any) –

• xid (str) –

• yid (str) –

• xid_sd (Optional[str]) –

• xid_se (Optional[str]) –

• yid_sd (Optional[str]) –

• yid_se (Optional[str]) –

• count (Optional[Union[int, str]]) –

• dataset (Optional[str]) –

• task (Optional[str]) –

• function (Optional[str]) –

is_task(self)

Check if FitData comes from a task (simulation).

Return type

bool

is_dataset(self)

Check if FitData comes from a dataset.

Return type

bool

is_function(self)

Check if FitData comes from a function.

Return type

bool

property dtype(self)

Get data type.

get_data(self)

Return actual data.

Numerical values are resolved using the executed simulation experiment.

Return type

FitDataInitialized

class sbmlsim.fit.objects.FitDataInitialized

Initialized FitData with actual data content.

Data is create from simulation experiment.

__str__(self)

Get string representation.

Return type

str