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fit

The fit settings, the metadata filters and the selection of the mappings.

The models of pkdb_models share their fit settings apart from the integrator tolerances (default_fit_settings). Every mapping_collections.py checks the type of the metadata of a mapping (mapping_metadata), reads the observable of a mapping (observable_id) and selects the training data by the values of the metadata (metadata_filter) or the observable (observable_filter). select_mappings selects the data of a fit with the paths of the model object, see sbmlsim.fit.helpers.FitMappings.select for the filters, outliers and validation data.

MetadataFilter dataclass 

MetadataFilter(metadata_type, allowed, excluded)

A filter of fit mappings by the values of their metadata.

A mapping passes when every allowed field has one of its values and no excluded field has one of its values. Build it with metadata_filter.

Attributes:

Name Type Description
metadata_type type[MappingMetaData]

the metadata class of the model.
allowed tuple[tuple[str, frozenset[Hashable]], ...]

the fields with the values a mapping must have.
excluded tuple[tuple[str, frozenset[Hashable]], ...]

the fields with the values a mapping must not have.

ObservableFilter dataclass 

ObservableFilter(observables)

A filter of fit mappings by their observable, see observable_id.

Attributes:

Name Type Description
observables frozenset[str]

the ids of the observables a mapping may have, the index of the data (Data.index).

default_fit_settings 

default_fit_settings(
    absolute_tolerance, relative_tolerance=1e-06
)

Get the settings of the fits of a model.

Normalized residuals with a linear loss; the curves are weighted by the weights of their mappings and by their number of points, the points by their errors (a mapping without errors is weighted with a CV of 0.5); the integrator takes variable steps; a data point at the time of a change after the start is compared with the state before the change (data_at_change), sbmlsim's default, written out so that a change of the default does not change the fits.

Parameters:

Name Type Description Default
absolute_tolerance float | AbsoluteTolerance

the absolute tolerance of the integrator, one value or an AbsoluteTolerance per kind and state.

 required
relative_tolerance float

the relative tolerance of the integrator.

 1e-06

Returns:

Type Description
FitSettings

The settings, stored with the result of a fit and read back by its report.

mapping_metadata 

mapping_metadata(fit_mapping, metadata_type)

Get the metadata of a fit mapping, checked against the type of the model.

Parameters:

Name Type Description Default
fit_mapping FitMapping

the fit mapping.

 required
metadata_type type[M]

the metadata class of the model.

 required

Returns:

Type Description
M

The metadata.

Raises:

Type Description
TypeError

if the mapping has no metadata of the type.

observable_id 

observable_id(fit_mapping)

Get the observable of a fit mapping, the index of its data.

The index of the y data of the observable (sbmlsim.data.Data.index): the selection without the brackets of a concentration, Cve_hctz for [Cve_hctz], Aurine_hctz for Aurine_hctz. sbmlsim 0.9 writes the concentration into the id of the data (<task>__conc__Cve_hctz), so the id is no longer read.

Parameters:

Name Type Description Default
fit_mapping FitMapping

the fit mapping.

 required

Returns:

Type Description
str

The id of the observable, e.g. Cve_hctz.

metadata_filter 

metadata_filter(metadata_type, *, excluded=None, **allowed)

Build a filter of fit mappings by the values of their metadata.

# not executed
filter_control = metadata_filter(
    HctzMappingMetaData,
    route={Route.PO, Route.IV},
    coadministration={Coadministration.NONE},
    excluded={"dosing": {Dosing.MULTIPLE}},
)

Parameters:

Name Type Description Default
metadata_type type[MappingMetaData]

the metadata class of the model, a dataclass.

 required
excluded Mapping[str, Collection[Hashable]] | None

the fields with the values a mapping must not have.

 None
**allowed Collection[Hashable]

the fields with the values a mapping must have.

 {}

Returns:

Type Description
MetadataFilter

The filter, called by the selection of the mappings of a fit.

Raises:

Type Description
TypeError

if the metadata class is no dataclass (or an instance of one), has no field of a given name, or the values of a field are not a collection (a single value, also a string or None), i.e. when the module of the filters is imported.
ValueError

for a filter without clauses, with an empty set of allowed values, which would select nothing, or with an empty set of excluded values, which would exclude nothing.

observable_filter 

observable_filter(observables)

Build a filter of fit mappings by their observable, e.g. the PK data.

Parameters:

Name Type Description Default
observables Collection[str]

the ids of the observables, the index of the data (Data.index), e.g. Cve_hctz.

 required

Returns:

Type Description
ObservableFilter

The filter.

Raises:

Type Description
TypeError

if the observables are a single id or not a collection.
ValueError

for no observables, which would select nothing.

select_mappings 

select_mappings(
    model,
    experiment_classes,
    filters=(),
    outliers=(),
    validation=(),
)

Select the data of a fit of a model, see sbmlsim.fit.helpers.FitMappings.

The experiments are created with the paths of the model object; the counts of the selected mappings per experiment and kind are printed.

Parameters:

Name Type Description Default
model PKPDModel

the model object.

 required
experiment_classes Iterable[type[SimulationExperiment]]

the experiments with the fit mappings.

 required
filters MappingFilter | Iterable[MappingFilter]

the filters of the training data.

 ()
outliers Iterable[str]

the keys of the training data which is not usable.

 ()
validation Iterable[str] | MappingFilter

the keys or a filter of the validation data.

 ()

Returns:

Type Description
dict[str, list[FitMappingCollection]]

The fit mapping collections of all kinds by experiment id.