amici.petab_objective¶

PEtab Objective¶

Functionality related to running simulations or evaluating the objective function as defined by a PEtab problem

Functions Summary

`aggregate_sllh`(amici_model, rdatas, …)	Aggregate likelihood gradient for all conditions, according to PEtab parameter mapping.
`create_edata_for_condition`(condition, …)	Get `amici.amici.ExpData` for the given PEtab condition.
`create_edatas`(amici_model, petab_problem[, …])	Create list of `amici.amici.ExpData` objects for PEtab problem.
`create_parameter_mapping`(petab_problem, …)	Generate AMICI specific parameter mapping.
`create_parameter_mapping_for_condition`(…)	Generate AMICI specific parameter mapping for condition.
`create_parameterized_edatas`(amici_model, …)	Create list of :class:amici.ExpData objects with parameters filled in.
`rdatas_to_measurement_df`(rdatas, model, …)	Create a measurement dataframe in the PEtab format from the passed rdatas and own information.
`rdatas_to_simulation_df`(rdatas, model, …)	Create a PEtab simulation dataframe from amici.ReturnDatas.
`simulate_petab`(petab_problem, amici_model[, …])	Simulate PEtab model.
`subset_dict`(full, *args)	Get subset of dictionary based on provided keys

Functions

amici.petab_objective.aggregate_sllh(amici_model, rdatas, parameter_mapping)[source]¶

Aggregate likelihood gradient for all conditions, according to PEtab parameter mapping.

Parameters

amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model from which rdatas were obtained.
rdatas (typing.Sequence[amici.numpy.ReturnDataView]) – Simulation results.
parameter_mapping (typing.Optional[amici.parameter_mapping.ParameterMapping]) –

PEtab parameter mapping to condition-specific
simulation parameters

Return type

typing.Optional[typing.Dict[str, float]]

Returns

aggregated sllh

amici.petab_objective.create_edata_for_condition(condition, amici_model, petab_problem, observable_ids)[source]¶

Get amici.amici.ExpData for the given PEtab condition.

Sets timepoints, observed data and sigmas.

Parameters

condition (typing.Union[typing.Dict, pandas.core.series.Series]) – pandas.DataFrame row with preequilibrationConditionId and simulationConditionId.
amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model
petab_problem (petab.problem.Problem) – Underlying PEtab problem
observable_ids (typing.List[str]) – List of observable IDs

Return type

amici.ExpData

Returns

ExpData instance.

amici.petab_objective.create_edatas(amici_model, petab_problem, simulation_conditions=None)[source]¶

Create list of amici.amici.ExpData objects for PEtab problem.

Parameters

amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model.
petab_problem (petab.problem.Problem) – Underlying PEtab problem.
simulation_conditions (typing.Union[pandas.core.frame.DataFrame, typing.Dict, None]) – Result of petab.get_simulation_conditions. Can be provided to save time if this has be obtained before.

Return type

typing.List[amici.ExpData]

Returns

List with one amici.amici.ExpData per simulation condition, with filled in timepoints and data.

amici.petab_objective.create_parameter_mapping(petab_problem, simulation_conditions, scaled_parameters, amici_model)[source]¶

Generate AMICI specific parameter mapping.

Parameters

petab_problem (petab.problem.Problem) – PEtab problem
simulation_conditions (typing.Union[pandas.core.frame.DataFrame, typing.Dict]) – Result of petab.get_simulation_conditions. Can be provided to save time if this has been obtained before.
scaled_parameters (bool) – If True, problem_parameters are assumed to be on the scale provided in the PEtab parameter table and will be unscaled. If False, they are assumed to be in linear scale.
amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model.

Return type

amici.parameter_mapping.ParameterMapping

Returns

List of the parameter mappings.

amici.petab_objective.create_parameter_mapping_for_condition(parameter_mapping_for_condition, condition, petab_problem, amici_model)[source]¶

Generate AMICI specific parameter mapping for condition.

Parameters

parameter_mapping_for_condition (typing.Tuple[typing.Dict[str, typing.Union[str, numbers.Number]], typing.Dict[str, typing.Union[str, numbers.Number]], typing.Dict[str, str], typing.Dict[str, str]]) – Preliminary parameter mapping for condition.
condition (typing.Union[pandas.core.series.Series, typing.Dict]) – pandas.DataFrame row with preequilibrationConditionId and simulationConditionId.
petab_problem (petab.problem.Problem) – Underlying PEtab problem.
amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model.

Return type

amici.parameter_mapping.ParameterMappingForCondition

Returns

The parameter and parameter scale mappings, for fixed preequilibration, fixed simulation, and variable simulation parameters, and then the respective scalings.

amici.petab_objective.create_parameterized_edatas(amici_model, petab_problem, problem_parameters, scaled_parameters=False, parameter_mapping=None, simulation_conditions=None)[source]¶

Create list of :class:amici.ExpData objects with parameters filled in.

Parameters

amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI Model assumed to be compatible with petab_problem.
petab_problem (petab.problem.Problem) – PEtab problem to work on.
problem_parameters (typing.Dict[str, numbers.Number]) – Run simulation with these parameters. If None, PEtab nominalValues will be used). To be provided as dict, mapping PEtab problem parameters to SBML IDs.
scaled_parameters (bool) – If True, problem_parameters are assumed to be on the scale provided in the PEtab parameter table and will be unscaled. If False, they are assumed to be in linear scale.
parameter_mapping (typing.Optional[amici.parameter_mapping.ParameterMapping]) – Optional precomputed PEtab parameter mapping for efficiency, as generated by create_parameter_mapping.
simulation_conditions (typing.Union[pandas.core.frame.DataFrame, typing.Dict, None]) – Result of petab.get_simulation_conditions. Can be provided to save time if this has been obtained before.

Return type

typing.List[amici.ExpData]

Returns

List with one amici.amici.ExpData per simulation condition, with filled in timepoints, data and parameters.

amici.petab_objective.rdatas_to_measurement_df(rdatas, model, measurement_df)[source]¶

Create a measurement dataframe in the PEtab format from the passed rdatas and own information.

Parameters

rdatas (typing.Sequence[amici.amici.ReturnData]) – A sequence of rdatas with the ordering of petab.get_simulation_conditions.
model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model used to generate rdatas.
measurement_df (pandas.core.frame.DataFrame) – PEtab measurement table used to generate rdatas.

Return type

pandas.core.frame.DataFrame

Returns

A dataframe built from the rdatas in the format of measurement_df.

amici.petab_objective.rdatas_to_simulation_df(rdatas, model, measurement_df)[source]¶

Create a PEtab simulation dataframe from amici.ReturnDatas.

See rdatas_to_measurement_df for details, only that model outputs will appear in column “simulation” instead of “measurement”.

Return type: pandas.core.frame.DataFrame

amici.petab_objective.simulate_petab(petab_problem, amici_model, solver=None, problem_parameters=None, simulation_conditions=None, edatas=None, parameter_mapping=None, scaled_parameters=False, log_level=30)[source]¶

Simulate PEtab model.

Parameters

petab_problem (petab.problem.Problem) – PEtab problem to work on.
amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI Model assumed to be compatible with petab_problem.
solver (typing.Optional[amici.amici.Solver]) – An AMICI solver. Will use default options if None.
problem_parameters (typing.Optional[typing.Dict[str, float]]) – Run simulation with these parameters. If None, PEtab nominalValues will be used). To be provided as dict, mapping PEtab problem parameters to SBML IDs.
simulation_conditions (typing.Union[pandas.core.frame.DataFrame, typing.Dict, None]) – Result of petab.get_simulation_conditions. Can be provided to save time if this has be obtained before. Not required if edatas and parameter_mapping are provided.
edatas (typing.Optional[typing.List[typing.Union[amici.ExpData, amici.amici.ExpDataPtr]]]) – Experimental data. Parameters are inserted in-place for simulation.
parameter_mapping (typing.Optional[amici.parameter_mapping.ParameterMapping]) – Optional precomputed PEtab parameter mapping for efficiency, as generated by create_parameter_mapping.
scaled_parameters (typing.Optional[bool]) – If True, problem_parameters are assumed to be on the scale provided in the PEtab parameter table and will be unscaled. If False, they are assumed to be in linear scale.
log_level (int) – Log level, see amici.logging module.

Return type

typing.Dict[str, typing.Any]

Returns

Dictionary of

cost function value (LLH),
const function sensitivity w.r.t. parameters (SLLH), (NOTE: Sensitivities are computed for the scaled parameters)
list of ReturnData (RDATAS),

corresponding to the different simulation conditions. For ordering of simulation conditions, see petab.Problem.get_simulation_conditions_from_measurement_df().

amici.petab_objective.subset_dict(full, *args)[source]¶

Get subset of dictionary based on provided keys

Parameters

full (typing.Dict[typing.Any, typing.Any]) – Dictionary to subset
args (typing.Collection[typing.Any]) – Collections of keys to be contained in the different subsets

Return type

typing.Iterator[typing.Dict[typing.Any, typing.Any]]

Returns

subsetted dictionary