API reference

pyPESTO

Parameter Estimation TOolbox for python.

class pypesto.AmiciObjective(amici_model: Union[amici.Model, amici.ModelPtr], amici_solver: Union[amici.Solver, amici.SolverPtr], edatas: Union[Sequence[amici.ExpData], amici.ExpData], max_sensi_order: int = None, x_ids: Sequence[str] = None, x_names: Sequence[str] = None, parameter_mapping: ParameterMapping = None, guess_steadystate: Optional[bool] = None, n_threads: int = 1, fim_for_hess: bool = True, amici_object_builder: pypesto.objective.amici.AmiciObjectBuilder = None, calculator: pypesto.objective.amici_calculator.AmiciCalculator = None)

Bases: pypesto.objective.base.ObjectiveBase

This class allows to create an objective directly from an amici model.

__init__(amici_model: Union[amici.Model, amici.ModelPtr], amici_solver: Union[amici.Solver, amici.SolverPtr], edatas: Union[Sequence[amici.ExpData], amici.ExpData], max_sensi_order: int = None, x_ids: Sequence[str] = None, x_names: Sequence[str] = None, parameter_mapping: ParameterMapping = None, guess_steadystate: Optional[bool] = None, n_threads: int = 1, fim_for_hess: bool = True, amici_object_builder: pypesto.objective.amici.AmiciObjectBuilder = None, calculator: pypesto.objective.amici_calculator.AmiciCalculator = None)

Constructor.

Parameters

• amici_model – The amici model.

• amici_solver – The solver to use for the numeric integration of the model.

• edatas – The experimental data. If a list is passed, its entries correspond to multiple experimental conditions.

• max_sensi_order – Maximum sensitivity order supported by the model. Defaults to 2 if the model was compiled with o2mode, otherwise 1.

• x_ids – Ids of optimization parameters. In the simplest case, this will be the AMICI model parameters (default).

• x_names – Names of optimization parameters.

• parameter_mapping – Mapping of optimization parameters to model parameters. Format as created by amici.petab_objective.create_parameter_mapping. The default is just to assume that optimization and simulation parameters coincide.

• guess_steadystate – Whether to guess steadystates based on previous steadystates and respective derivatives. This option may lead to unexpected results for models with conservation laws and should accordingly be deactivated for those models.

• n_threads – Number of threads that are used for parallelization over experimental conditions. If amici was not installed with openMP support this option will have no effect.

• fim_for_hess – Whether to use the FIM whenever the Hessian is requested. This only applies with forward sensitivities. With adjoint sensitivities, the true Hessian will be used, if available. FIM or Hessian will only be exposed if max_sensi_order>1.

• amici_object_builder – AMICI object builder. Allows recreating the objective for pickling, required in some parallelization schemes.

• calculator – Performs the actual calculation of the function values and derivatives.

apply_custom_timepoints()

Apply custom timepoints, if applicable.

See the set_custom_timepoints method for more information.

apply_steadystate_guess(condition_ix: int, x_dct: Dict)

Use the stored steadystate as well as the respective sensitivity ( if available) and parameter value to approximate the steadystate at the current parameters using a zeroth or first order taylor approximation: x_ss(x’) = x_ss(x) [+ dx_ss/dx(x)*(x’-x)]

call_unprocessed(x, sensi_orders, mode, edatas=None, parameter_mapping=None)

Call objective function without pre- or post-processing and formatting.

Parameters

• x – The parameters for which to evaluate the objective function.

• sensi_orders – Specifies which sensitivities to compute, e.g. (0,1) -> fval, grad.

• mode – Whether to compute function values or residuals.

Returns

A dict containing the results.

Return type

result

check_mode(mode)

Check if the objective is able to compute in the requested mode.

Parameters

mode – Whether to compute function values or residuals.

Returns

Boolean indicating whether mode is supported

Return type

flag

check_sensi_orders(sensi_orders, mode) → bool

Check if the objective is able to compute the requested sensitivities.

Parameters

• sensi_orders – Specifies which sensitivities to compute, e.g. (0,1) -> fval, grad.

• mode – Whether to compute function values or residuals.

Returns

Boolean indicating whether combination of sensi_orders and mode is supported

Return type

flag

initialize()

Initialize the objective function. This function is used at the beginning of an analysis, e.g. optimization, and can e.g. reset the objective memory. By default does nothing.

par_arr_to_dct(x: Sequence[float]) → Dict[str, float]

Create dict from parameter vector.

reset_steadystate_guesses()

Resets all steadystate guess data

set_custom_timepoints(timepoints: Optional[Sequence[Sequence[Union[float, int]]]] = None, timepoints_global: Optional[Sequence[Union[float, int]]] = None) → pypesto.objective.amici.AmiciObjective

Create a copy of this objective that will be evaluated at custom timepoints.

The intended use is to aid in predictions at unmeasured timepoints.

Parameters

• timepoints – The outer sequence should contain a sequence of timepoints for each experimental condition.

• timepoints_global – A sequence of timepoints that will be used for all experimental conditions.

Returns

Return type

The customized copy of this objective.

store_steadystate_guess(condition_ix: int, x_dct: Dict, rdata: amici.ReturnData)

Store condition parameter, steadystate and steadystate sensitivity in steadystate_guesses if steadystate guesses are enabled for this condition

class pypesto.AmiciPredictor(amici_objective: pypesto.objective.amici.AmiciObjective, amici_output_fields: Optional[Sequence[str]] = None, post_processor: Optional[Callable] = None, post_processor_sensi: Optional[Callable] = None, post_processor_time: Optional[Callable] = None, max_chunk_size: Optional[int] = None, output_ids: Optional[Sequence[str]] = None, condition_ids: Optional[Sequence[str]] = None)

Bases: object

Do forward simulations (predictions) with parameter vectors, for an AMICI model. The user may supply post-processing methods. If post-processing methods are supplied, and a gradient of the prediction is requested, then the sensitivities of the AMICI model must also be post-processed. There are no checks here to ensure that the sensitivities are correctly post-processed, this is explicitly left to the user. There are also no safeguards if the postprocessor routines fail. This may happen if, e.g., a call to Amici fails, and no timepoints, states or observables are returned. As the AmiciPredictor is agnostic about the dimension of the postprocessor and also the dimension of the postprocessed output, these checks are also left to the user. An example for such a check is provided in the default output (see _default_output()).

__call__(x: numpy.ndarray, sensi_orders: Tuple[int, …] = (0), mode: str = 'mode_fun', output_file: str = '', output_format: str = 'csv') → pypesto.predict.result.PredictionResult

Simulate a model for a certain prediction function. This method relies on the AmiciObjective, which is underlying, but allows the user to apply any post-processing of the results, the sensitivities, and the timepoints.

Parameters

• x – The parameters for which to evaluate the prediction function.

• sensi_orders – Specifies which sensitivities to compute, e.g. (0,1) -> fval, grad.

• mode – Whether to compute function values or residuals.

• output_file – Path to an output file.

• output_format – Either ‘csv’, ‘h5’. If an output file is specified, this routine will return a csv file, created from a DataFrame, or an h5 file, created from a dict.

Returns

PredictionResult object containing timepoints, outputs, and output_sensitivities if requested

Return type

results

__init__(amici_objective: pypesto.objective.amici.AmiciObjective, amici_output_fields: Optional[Sequence[str]] = None, post_processor: Optional[Callable] = None, post_processor_sensi: Optional[Callable] = None, post_processor_time: Optional[Callable] = None, max_chunk_size: Optional[int] = None, output_ids: Optional[Sequence[str]] = None, condition_ids: Optional[Sequence[str]] = None)

Constructor.

Parameters

• amici_objective – An objective object, which will be used to get model simulations

• amici_output_fields – keys that exist in the return data object from AMICI, which should be available for the post-processors

• post_processor – A callable function which applies postprocessing to the simulation results and possibly defines different outputs than those of the amici model. Default are the observables (pypesto.predict.constants.AMICI_Y) of the AMICI model. This method takes a list of dicts (with the returned fields pypesto.predict.constants.AMICI_T, pypesto.predict.constants.AMICI_X, and pypesto.predict.constants.AMICI_Y of the AMICI ReturnData objects) as input. Safeguards for, e.g., failure of AMICI are left to the user.

• post_processor_sensi – A callable function which applies postprocessing to the sensitivities of the simulation results. Defaults to the observable sensitivities of the AMICI model. This method takes a list of dicts (with the returned fields pypesto.predict.constants.AMICI_T, pypesto.predict.constants.AMICI_X, pypesto.predict.constants.AMICI_Y, pypesto.predict.constants.AMICI_SX, and pypesto.predict.constants.AMICI_SY of the AMICI ReturnData objects) as input. Safeguards for, e.g., failure of AMICI are left to the user.

• post_processor_time – A callable function which applies postprocessing to the timepoints of the simulations. Defaults to the timepoints of the amici model. This method takes a list of dicts (with the returned field pypesto.predict.constants.AMICI_T of the amici ReturnData objects) as input. Safeguards for, e.g., failure of AMICI are left to the user.

• max_chunk_size – In some cases, we don’t want to compute all predictions at once when calling the prediction function, as this might not fit into the memory for large datasets and models. Here, the user can specify a maximum chunk size of conditions, which should be simulated at a time. Defaults to None, meaning that all conditions will be simulated.

• output_ids – IDs of outputs, as post-processing allows the creation of customizable outputs, which may not coincide with those from the AMICI model (defaults to AMICI observables).

• condition_ids – List of identifiers for the conditions of the edata objects of the amici objective, will be passed to the PredictionResult at call.

class pypesto.CsvHistory(file: str, x_names: Optional[Sequence[str]] = None, options: Optional[Union[pypesto.objective.history.HistoryOptions, Dict]] = None, load_from_file: bool = False)

Bases: pypesto.objective.history.History

Stores a representation of the history in a CSV file.

Parameters

• file – CSV file name.

• x_names – Parameter names.

• options – History options.

• load_from_file – If True, history will be initialized from data in the specified file

__init__(file: str, x_names: Optional[Sequence[str]] = None, options: Optional[Union[pypesto.objective.history.HistoryOptions, Dict]] = None, load_from_file: bool = False)

Initialize self. See help(type(self)) for accurate signature.

finalize()

Finalize history. Called after a run.

get_chi2_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Chi2 values.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_fval_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Function values.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_grad_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Gradients.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_hess_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Hessians.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_res_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Residuals.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_schi2_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Chi2 sensitivities.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_sres_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Residual sensitivities.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_time_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Cumulative execution times.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_x_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Parameters.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

update(x: numpy.ndarray, sensi_orders: Tuple[int, …], mode: str, result: Dict[str, Union[float, numpy.ndarray]]) → None

Update history after a function evaluation.

Parameters

• x – The parameter vector.

• sensi_orders – The sensitivity orders computed.

• mode – The objective function mode computed (function value or residuals).

• result – The objective function values for parameters x, sensitivities sensi_orders and mode mode.

class pypesto.Hdf5History(id: str, file: str, options: Optional[Union[pypesto.objective.history.HistoryOptions, Dict]] = None)

Bases: pypesto.objective.history.History

Stores a representation of the history in an HDF5 file.

Parameters

• id – Id of the history

• file – HDF5 file name.

• options – History options.

__init__(id: str, file: str, options: Optional[Union[pypesto.objective.history.HistoryOptions, Dict]] = None)

Initialize self. See help(type(self)) for accurate signature.

finalize()

Finalize history. Called after a run.

get_chi2_trace() → Sequence[numpy.ndarray]

Chi2 values.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_fval_trace() → Sequence[float]

Function values.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_grad_trace() → Sequence[numpy.ndarray]

Gradients.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_hess_trace() → Sequence[numpy.ndarray]

Hessians.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_history_directory()

get_res_trace() → Sequence[numpy.ndarray]

Residuals.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_schi2_trace(t: Optional[int] = None) → Sequence[numpy.ndarray]

Chi2 sensitivities.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_sres_trace() → Sequence[numpy.ndarray]

Residual sensitivities.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_time_trace(t: Optional[int] = None) → Sequence[numpy.ndarray]

Cumulative execution times.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_x_trace() → Sequence[numpy.ndarray]

Parameters.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

static load(id: str, file: str)

Loads the History object from memory.

property n_fval

Number of function evaluations.

property n_grad

Number of gradient evaluations.

property n_hess

Number of Hessian evaluations.

property n_res

Number of residual evaluations.

property n_sres

Number or residual sensitivity evaluations.

property trace_save_iter

update(x: numpy.ndarray, sensi_orders: Tuple[int, …], mode: str, result: Dict[str, Union[float, numpy.ndarray]]) → None

Update history after a function evaluation.

Parameters

• x – The parameter vector.

• sensi_orders – The sensitivity orders computed.

• mode – The objective function mode computed (function value or residuals).

• result – The objective function values for parameters x, sensitivities sensi_orders and mode mode.

class pypesto.History(options: Optional[Union[pypesto.objective.history.HistoryOptions, Dict]] = None)

Bases: pypesto.objective.history.HistoryBase

Tracks numbers of function evaluations only, no trace.

Parameters

options – History options.

__init__(options: Optional[Union[pypesto.objective.history.HistoryOptions, Dict]] = None)

Initialize self. See help(type(self)) for accurate signature.

finalize()

Finalize history. Called after a run.

property n_fval

Number of function evaluations.

property n_grad

Number of gradient evaluations.

property n_hess

Number of Hessian evaluations.

property n_res

Number of residual evaluations.

property n_sres

Number or residual sensitivity evaluations.

property start_time

Start time.

update(x: numpy.ndarray, sensi_orders: Tuple[int, …], mode: str, result: Dict[str, Union[float, numpy.ndarray]]) → None

Update history after a function evaluation.

Parameters

• x – The parameter vector.

• sensi_orders – The sensitivity orders computed.

• mode – The objective function mode computed (function value or residuals).

• result – The objective function values for parameters x, sensitivities sensi_orders and mode mode.

class pypesto.HistoryBase

Bases: abc.ABC

Abstract base class for history objects.

Can be used as a dummy history, but does not implement any history functionality.

finalize()

Finalize history. Called after a run.

get_chi2_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[float], float]

Chi2 values.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_fval_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[float], float]

Function values.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_grad_trace(ix: Optional[Union[int, Sequence[int]]] = None) → Union[Sequence[Union[numpy.ndarray, np.nan]], numpy.ndarray, np.nan]

Gradients.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_hess_trace(ix: Optional[Union[int, Sequence[int]]] = None) → Union[Sequence[Union[numpy.ndarray, np.nan]], numpy.ndarray, np.nan]

Hessians.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_res_trace(ix: Optional[Union[int, Sequence[int]]] = None) → Union[Sequence[Union[numpy.ndarray, np.nan]], numpy.ndarray, np.nan]

Residuals.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_schi2_trace(ix: Optional[Union[int, Sequence[int]]] = None) → Union[Sequence[Union[numpy.ndarray, np.nan]], numpy.ndarray, np.nan]

Chi2 sensitivities.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_sres_trace(ix: Optional[Union[int, Sequence[int]]] = None) → Union[Sequence[Union[numpy.ndarray, np.nan]], numpy.ndarray, np.nan]

Residual sensitivities.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_time_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[float], float]

Cumulative execution times.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_x_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[numpy.ndarray], numpy.ndarray]

Parameters.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

property n_fval

Number of function evaluations.

property n_grad

Number of gradient evaluations.

property n_hess

Number of Hessian evaluations.

property n_res

Number of residual evaluations.

property n_sres

Number or residual sensitivity evaluations.

property start_time

Start time.

update(x: numpy.ndarray, sensi_orders: Tuple[int, …], mode: str, result: Dict[str, Union[float, numpy.ndarray]]) → None

Update history after a function evaluation.

Parameters

• x – The parameter vector.

• sensi_orders – The sensitivity orders computed.

• mode – The objective function mode computed (function value or residuals).

• result – The objective function values for parameters x, sensitivities sensi_orders and mode mode.

class pypesto.HistoryOptions(trace_record: bool = False, trace_record_grad: bool = True, trace_record_hess: bool = True, trace_record_res: bool = True, trace_record_sres: bool = True, trace_record_chi2: bool = True, trace_record_schi2: bool = True, trace_save_iter: int = 10, storage_file: Optional[str] = None)

Bases: dict

Options for the objective that are used in optimization, profiling and sampling.

In addition implements a factory pattern to generate history objects.

Parameters

• trace_record – Flag indicating whether to record the trace of function calls. The trace_record_* flags only become effective if trace_record is True. Default: False.

• trace_record_grad – Flag indicating whether to record the gradient in the trace. Default: True.

• trace_record_hess – Flag indicating whether to record the Hessian in the trace. Default: False.

• trace_record_res – Flag indicating whether to record the residual in the trace. Default: False.

• trace_record_sres – Flag indicating whether to record the residual sensitivities in the trace. Default: False.

• trace_record_chi2 – Flag indicating whether to record the chi2 in the trace. Default: True.

• trace_record_schi2 – Flag indicating whether to record the chi2 sensitivities in the trace. Default: True.

• trace_save_iter – After how many iterations to store the trace.

• storage_file – File to save the history to. Can be any of None, a “{filename}.csv”, or a “{filename}.hdf5” file. Depending on the values, the create_history method creates the appropriate object. Occurrences of “{id}” in the file name are replaced by the id upon creation of a history, if applicable.

__init__(trace_record: bool = False, trace_record_grad: bool = True, trace_record_hess: bool = True, trace_record_res: bool = True, trace_record_sres: bool = True, trace_record_chi2: bool = True, trace_record_schi2: bool = True, trace_save_iter: int = 10, storage_file: Optional[str] = None)

Initialize self. See help(type(self)) for accurate signature.

static assert_instance(maybe_options: Union[pypesto.objective.history.HistoryOptions, Dict]) → pypesto.objective.history.HistoryOptions

Returns a valid options object.

Parameters

maybe_options (HistoryOptions or dict) –

create_history(id: str, x_names: Sequence[str]) → pypesto.objective.history.History

Factory method creating a History object.

Parameters

• id – Identifier for the history.

• x_names – Parameter names.

class pypesto.MemoryHistory(options: Optional[Union[pypesto.objective.history.HistoryOptions, Dict]] = None)

Bases: pypesto.objective.history.History

Tracks numbers of function evaluations and keeps an in-memory trace of function evaluations.

Parameters

options – History options.

__init__(options: Optional[Union[pypesto.objective.history.HistoryOptions, Dict]] = None)

Initialize self. See help(type(self)) for accurate signature.

get_chi2_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Chi2 values.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_fval_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Function values.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_grad_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Gradients.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_hess_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Hessians.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_res_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Residuals.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_schi2_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Chi2 sensitivities.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_sres_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Residual sensitivities.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_time_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Cumulative execution times.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

get_x_trace(ix: Optional[Union[Sequence[int], int]] = None) → Union[Sequence[Union[float, numpy.ndarray, np.nan]], float, numpy.ndarray, np.nan]

Parameters.

Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened.

update(x: numpy.ndarray, sensi_orders: Tuple[int, …], mode: str, result: Dict[str, Union[float, numpy.ndarray]]) → None

Update history after a function evaluation.

Parameters

• x – The parameter vector.

• sensi_orders – The sensitivity orders computed.

• mode – The objective function mode computed (function value or residuals).

• result – The objective function values for parameters x, sensitivities sensi_orders and mode mode.

class pypesto.NegLogPriors(objectives: Sequence[pypesto.objective.base.ObjectiveBase], x_names: Optional[Sequence[str]] = None)

Bases: pypesto.objective.aggregated.AggregatedObjective

Aggregates different forms of negative log-prior distributions.

Allows to distinguish priors from the likelihood by testing the type of an objective.

Consists basically of a list of individual negative log-priors, given in self.objectives.

class pypesto.Objective(fun: Optional[Callable] = None, grad: Optional[Union[Callable, bool]] = None, hess: Optional[Callable] = None, hessp: Optional[Callable] = None, res: Optional[Callable] = None, sres: Optional[Union[Callable, bool]] = None, x_names: Optional[Sequence[str]] = None)

Bases: pypesto.objective.base.ObjectiveBase

The objective class allows the user explicitely specify functions that compute the function value and/or residuals as well as respective derivatives.

Parameters

• fun –

  The objective function to be minimized. If it only computes the objective function value, it should be of the form

  fun(x) -> float

  where x is an 1-D array with shape (n,), and n is the parameter space dimension.

• grad –

  Method for computing the gradient vector. If it is a callable, it should be of the form

  grad(x) -> array_like, shape (n,).

  If its value is True, then fun should return the gradient as a second output.

• hess –

  Method for computing the Hessian matrix. If it is a callable, it should be of the form

  hess(x) -> array, shape (n,n).

  If its value is True, then fun should return the gradient as a second, and the Hessian as a third output, and grad should be True as well.

• hessp –

  Method for computing the Hessian vector product, i.e.

  hessp(x, v) -> array_like, shape (n,)

  computes the product H*v of the Hessian of fun at x with v.

• res –

  Method for computing residuals, i.e.

  res(x) -> array_like, shape(m,).

• sres –

  Method for computing residual sensitivities. If its is a callable, it should be of the form

  sres(x) -> array, shape (m,n).

  If its value is True, then res should return the residual sensitivities as a second output.

• x_names – Parameter names. None if no names provided, otherwise a list of str, length dim_full (as in the Problem class). Can be read by the problem.

__init__(fun: Optional[Callable] = None, grad: Optional[Union[Callable, bool]] = None, hess: Optional[Callable] = None, hessp: Optional[Callable] = None, res: Optional[Callable] = None, sres: Optional[Union[Callable, bool]] = None, x_names: Optional[Sequence[str]] = None)

Initialize self. See help(type(self)) for accurate signature.

call_unprocessed(x, sensi_orders, mode)

Call objective function without pre- or post-processing and formatting.

Returns

A dict containing the results.

Return type

result

check_mode(mode)

Check if the objective is able to compute in the requested mode.

Parameters

mode – Whether to compute function values or residuals.

Returns

Boolean indicating whether mode is supported

Return type

flag

check_sensi_orders(sensi_orders, mode)

Check if the objective is able to compute the requested sensitivities.

Parameters

• sensi_orders – Specifies which sensitivities to compute, e.g. (0,1) -> fval, grad.

• mode – Whether to compute function values or residuals.

Returns

Boolean indicating whether combination of sensi_orders and mode is supported

Return type

flag

property has_fun

property has_grad

property has_hess

property has_hessp

property has_res

property has_sres

class pypesto.ObjectiveBase(x_names: Optional[Sequence[str]] = None)

Bases: abc.ABC

The objective class is a simple wrapper around the objective function, giving a standardized way of calling. Apart from that, it manages several things including fixing of parameters and history.

The objective function is assumed to be in the format of a cost function, log-likelihood function, or log-posterior function. These functions are subject to minimization. For profiling and sampling, the sign is internally flipped, all returned and stored values are however given as returned by this objective function. If maximization is to be performed, the sign should be flipped before creating the objective function.

Parameters

x_names – Parameter names that can be optionally used in, e.g., history or gradient checks

history

For storing the call history. Initialized by the methods, e.g. the optimizer, in initialize_history().

pre_post_processor

Preprocess input values to and postprocess output values from __call__. Configured in update_from_problem().

__call__(x: numpy.ndarray, sensi_orders: Tuple[int, …] = (0), mode: str = 'mode_fun', return_dict: bool = False, **kwargs) → Union[float, numpy.ndarray, Tuple, Dict[str, Union[float, numpy.ndarray, Dict]]]

Method to obtain arbitrary sensitivities. This is the central method which is always called, also by the get_* methods.

There are different ways in which an optimizer calls the objective function, and in how the objective function provides information (e.g. derivatives via separate functions or along with the function values). The different calling modes increase efficiency in space and time and make the objective flexible.

Parameters

• x – The parameters for which to evaluate the objective function.

• sensi_orders – Specifies which sensitivities to compute, e.g. (0,1) -> fval, grad.

• mode – Whether to compute function values or residuals.

• return_dict – If False (default), the result is a Tuple of the requested values in the requested order. Tuples of length one are flattened. If True, instead a dict is returned which can carry further information.

Returns

By default, this is a tuple of the requested function values and derivatives in the requested order (if only 1 value, the tuple is flattened). If return_dict, then instead a dict is returned with function values and derivatives indicated by ids.

Return type

result

__init__(x_names: Optional[Sequence[str]] = None)

Initialize self. See help(type(self)) for accurate signature.

abstract call_unprocessed(x: numpy.ndarray, sensi_orders: Tuple[int, …], mode: str, **kwargs) → Dict[str, Union[float, numpy.ndarray, Dict]]

Call objective function without pre- or post-processing and formatting.

Parameters

• x – The parameters for which to evaluate the objective function.

• sensi_orders – Specifies which sensitivities to compute, e.g. (0,1) -> fval, grad.

• mode – Whether to compute function values or residuals.

Returns

A dict containing the results.

Return type

result

check_grad(x: numpy.ndarray, x_indices: Optional[Sequence[int]] = None, eps: float = 1e-05, verbosity: int = 1, mode: str = 'mode_fun', detailed: bool = False) → pandas.core.frame.DataFrame

Compare gradient evaluation: Firstly approximate via finite differences, and secondly use the objective gradient.

Parameters

• x – The parameters for which to evaluate the gradient.

• x_indices – Indices for which to compute gradients. Default: all.

• eps – Finite differences step size.

• verbosity – Level of verbosity for function output. 0: no output, 1: summary for all parameters, 2: summary for individual parameters.

• mode – Residual (MODE_RES) or objective function value (MODE_FUN) computation mode.

• detailed – Toggle whether additional values are returned. Additional values are function values, and the central difference weighted by the difference in output from all methods (standard deviation and mean).

Returns

gradient, finite difference approximations and error estimates.

Return type

result

check_grad_multi_eps(*args, multi_eps: Optional[Iterable] = None, label: str = 'rel_err', **kwargs)

Equivalent to the ObjectiveBase.check_grad method, except multiple finite difference step sizes are tested. The result contains the lowest finite difference for each parameter, and the corresponding finite difference step size.

Parameters

• ObjectiveBase.check_grad method parameters. (All) –

• multi_eps – The finite difference step sizes to be tested.

• label – The label of the column that will be minimized for each parameter. Valid options are the column labels of the dataframe returned by the ObjectiveBase.check_grad method.

abstract check_mode(mode) → bool

Check if the objective is able to compute in the requested mode.

Parameters

mode – Whether to compute function values or residuals.

Returns

Boolean indicating whether mode is supported

Return type

flag

abstract check_sensi_orders(sensi_orders, mode) → bool

Check if the objective is able to compute the requested sensitivities.

Parameters

• sensi_orders – Specifies which sensitivities to compute, e.g. (0,1) -> fval, grad.

• mode – Whether to compute function values or residuals.

Returns

Boolean indicating whether combination of sensi_orders and mode is supported

Return type

flag

get_fval(x: numpy.ndarray) → float

Get the function value at x.

get_grad(x: numpy.ndarray) → numpy.ndarray

Get the gradient at x.

get_hess(x: numpy.ndarray) → numpy.ndarray

Get the Hessian at x.

get_res(x: numpy.ndarray) → numpy.ndarray

Get the residuals at x.

get_sres(x: numpy.ndarray) → numpy.ndarray

Get the residual sensitivities at x.

property has_fun

property has_grad

property has_hess

property has_hessp

property has_res

property has_sres

initialize()

Initialize the objective function. This function is used at the beginning of an analysis, e.g. optimization, and can e.g. reset the objective memory. By default does nothing.

static output_to_tuple(sensi_orders: Tuple[int, …], mode: str, **kwargs: Union[float, numpy.ndarray]) → Tuple

Return values as requested by the caller, since usually only a subset is demanded. One output is returned as-is, more than one output are returned as a tuple in order (fval, grad, hess).

update_from_problem(dim_full: int, x_free_indices: Sequence[int], x_fixed_indices: Sequence[int], x_fixed_vals: Sequence[float])

Handle fixed parameters. Later, the objective will be given parameter vectors x of dimension dim, which have to be filled up with fixed parameter values to form a vector of dimension dim_full >= dim. This vector is then used to compute function value and derivatives. The derivatives must later be reduced again to dimension dim.

This is so as to make the fixing of parameters transparent to the caller.

The methods preprocess, postprocess are overwritten for the above functionality, respectively.

Parameters

• dim_full – Dimension of the full vector including fixed parameters.

• x_free_indices – Vector containing the indices (zero-based) of free parameters (complimentary to x_fixed_indices).

• x_fixed_indices – Vector containing the indices (zero-based) of parameter components that are not to be optimized.

• x_fixed_vals – Vector of the same length as x_fixed_indices, containing the values of the fixed parameters.

class pypesto.OptimizeResult

Bases: object

Result of the minimize() function.

__init__()

Initialize self. See help(type(self)) for accurate signature.

append(optimizer_result: optimize.OptimizerResult)

Append an optimizer result to the result object.

Parameters

optimizer_result – The result of one (local) optimizer run.

as_dataframe(keys=None) → pandas.core.frame.DataFrame

Get as pandas DataFrame. If keys is a list, return only the specified values.

as_list(keys=None) → Sequence

Get as list. If keys is a list, return only the specified values.

Parameters

keys (list(str), optional) – Labels of the field to extract.

get_for_key(key) → list

Extract the list of values for the specified key as a list.

sort()

Sort the optimizer results by function value fval (ascending).

class pypesto.OptimizerHistory(history: pypesto.objective.history.History, x0: numpy.ndarray, generate_from_history: bool = False)

Bases: object

Objective call history. Container around a History object, which keeps track of optimal values.

fval0, fval_min

Initial and best function value found.

chi20, chi2_min

Initial and best chi2 value found.

x0, x_min

Initial and best parameters found.

grad_min

gradient for best parameters

hess_min

hessian (approximation) for best parameters

res_min

residuals for best parameters

sres_min

residual sensitivities for best parameters

Parameters

• history – History object to attach to this container. This history object implements the storage of the actual history.

• x0 – Initial values for optimization

• generate_from_history – If set to true, this function will try to fill attributes of this function based on the provided history

__init__(history: pypesto.objective.history.History, x0: numpy.ndarray, generate_from_history: bool = False) → None

Initialize self. See help(type(self)) for accurate signature.

extract_from_history(var, ix)

finalize()

update(x: numpy.ndarray, sensi_orders: Tuple[int], mode: str, result: Dict[str, Union[float, numpy.ndarray]]) → None

Update history and best found value.

class pypesto.PredictionConditionResult(timepoints: numpy.ndarray, output_ids: Sequence[str], output: Optional[numpy.ndarray] = None, output_sensi: Optional[numpy.ndarray] = None, x_names: Optional[Sequence[str]] = None)

Bases: object

This class is a light-weight wrapper for the prediction of one simulation condition of an amici model. It should provide a common api how amici predictions should look like in pyPESTO.

__init__(timepoints: numpy.ndarray, output_ids: Sequence[str], output: Optional[numpy.ndarray] = None, output_sensi: Optional[numpy.ndarray] = None, x_names: Optional[Sequence[str]] = None)

Constructor.

Parameters

• timepoints – Output timepoints for this simulation condition

• output_ids – IDs of outputs for this simulation condition

• outputs – Postprocessed outputs (ndarray)

• outputs_sensi – Sensitivities of postprocessed outputs (ndarray)

• x_names – IDs of model parameter w.r.t to which sensitivities were computed

class pypesto.PredictionResult(conditions: Sequence[Union[pypesto.predict.result.PredictionConditionResult, Dict]], condition_ids: Optional[Sequence[str]] = None, comment: Optional[str] = None)

Bases: object

This class is a light-weight wrapper around predictions from pyPESTO made via an amici model. It’s only purpose is to have fixed format/api, how prediction results should be stored, read, and handled: as predictions are a very flexible format anyway, they should at least have a common definition, which allows to work with them in a reasonable way.

__init__(conditions: Sequence[Union[pypesto.predict.result.PredictionConditionResult, Dict]], condition_ids: Optional[Sequence[str]] = None, comment: Optional[str] = None)

Constructor.

Parameters

• conditions – A list of PredictionConditionResult objects or dicts

• condition_ids – IDs or names of the simulation conditions, which belong to this prediction (e.g., PEtab uses tuples of preequilibration condition and simulation conditions)

• comment – An additional note, which can be attached to this prediction

write_to_csv(output_file: str)

This method saves predictions to a csv file.

Parameters

output_file – path to file/folder to which results will be written

write_to_h5(output_file: str, base_path: Optional[str] = None)

This method saves predictions to an h5 file. It appends to the file if the file already exists.

Parameters

• output_file – path to file/folder to which results will be written

• base_path – base path in the h5 file

class pypesto.Problem(objective: pypesto.objective.base.ObjectiveBase, lb: Union[numpy.ndarray, List[float]], ub: Union[numpy.ndarray, List[float]], dim_full: Optional[int] = None, x_fixed_indices: Optional[Union[Iterable[SupportsInt], SupportsInt]] = None, x_fixed_vals: Optional[Union[Iterable[SupportsFloat], SupportsFloat]] = None, x_guesses: Optional[Iterable[float]] = None, startpoint_method: Optional[Callable] = None, x_names: Optional[Iterable[str]] = None, x_scales: Optional[Iterable[str]] = None, x_priors_defs: Optional[pypesto.objective.priors.NegLogPriors] = None, lb_init: Optional[Union[numpy.ndarray, List[float]]] = None, ub_init: Optional[Union[numpy.ndarray, List[float]]] = None)

Bases: object

The problem formulation. A problem specifies the objective function, boundaries and constraints, parameter guesses as well as the parameters which are to be optimized.

Parameters

• objective – The objective function for minimization. Note that a shallow copy is created.

• lb – The lower and upper bounds for optimization. For unbounded directions set to +-inf.

• ub – The lower and upper bounds for optimization. For unbounded directions set to +-inf.

• lb_init – The lower and upper bounds for initialization, typically for defining search start points. If not set, set to lb, ub.

• ub_init – The lower and upper bounds for initialization, typically for defining search start points. If not set, set to lb, ub.

• dim_full – The full dimension of the problem, including fixed parameters.

• x_fixed_indices – Vector containing the indices (zero-based) of parameter components that are not to be optimized.

• x_fixed_vals – Vector of the same length as x_fixed_indices, containing the values of the fixed parameters.

• x_guesses – Guesses for the parameter values, shape (g, dim), where g denotes the number of guesses. These are used as start points in the optimization.

• startpoint_method – Callable. startpoint_method(n_starts) returns a n_starts x n_free_indices array of initial values for the optimization.

• x_names – Parameter names that can be optionally used e.g. in visualizations. If objective.get_x_names() is not None, those values are used, else the values specified here are used if not None, otherwise the variable names are set to [‘x0’, … ‘x{dim_full}’]. The list must always be of length dim_full.

• x_scales – Parameter scales can be optionally given and are used e.g. in visualisation and prior generation. Currently the scales ‘lin’, ‘log`and ‘log10’ are supported.

• x_priors_defs – Definitions of priors for parameters. Types of priors, and their required and optional parameters, are described in the Prior class.

Notes

On the fixing of parameter values:

The number of parameters dim_full the objective takes as input must be known, so it must be either lb a vector of that size, or dim_full specified as a parameter.

All vectors are mapped to the reduced space of dimension dim in __init__, regardless of whether they were in dimension dim or dim_full before. If the full representation is needed, the methods get_full_vector() and get_full_matrix() can be used.

__init__(objective: pypesto.objective.base.ObjectiveBase, lb: Union[numpy.ndarray, List[float]], ub: Union[numpy.ndarray, List[float]], dim_full: Optional[int] = None, x_fixed_indices: Optional[Union[Iterable[SupportsInt], SupportsInt]] = None, x_fixed_vals: Optional[Union[Iterable[SupportsFloat], SupportsFloat]] = None, x_guesses: Optional[Iterable[float]] = None, startpoint_method: Optional[Callable] = None, x_names: Optional[Iterable[str]] = None, x_scales: Optional[Iterable[str]] = None, x_priors_defs: Optional[pypesto.objective.priors.NegLogPriors] = None, lb_init: Optional[Union[numpy.ndarray, List[float]]] = None, ub_init: Optional[Union[numpy.ndarray, List[float]]] = None)

Initialize self. See help(type(self)) for accurate signature.

property dim

fix_parameters(parameter_indices: Union[Iterable[SupportsInt], SupportsInt], parameter_vals: Union[Iterable[SupportsFloat], SupportsFloat]) → None

Fix specified parameters to specified values

full_index_to_free_index(full_index: int)

Calculate index in reduced vector from index in full vector.

Parameters

full_index (The index in the full vector.) –

Returns

free_index

Return type

The index in the free vector.

get_full_matrix(x: Optional[numpy.ndarray]) → Optional[numpy.ndarray]

Map matrix from dim to dim_full. Usually used for hessian.

Parameters

x (array_like, shape=(dim, dim)) – The matrix in dimension dim.

get_full_vector(x: Optional[numpy.ndarray], x_fixed_vals: Optional[Iterable[float]] = None) → Optional[numpy.ndarray]

Map vector from dim to dim_full. Usually used for x, grad.

Parameters

• x (array_like, shape=(dim,)) – The vector in dimension dim.

• x_fixed_vals (array_like, ndim=1, optional) – The values to be used for the fixed indices. If None, then nans are inserted. Usually, None will be used for grad and problem.x_fixed_vals for x.

get_reduced_matrix(x_full: Optional[numpy.ndarray]) → Optional[numpy.ndarray]

Map matrix from dim_full to dim, i.e. delete fixed indices.

Parameters

x_full (array_like, ndim=2) – The matrix in dimension dim_full.

get_reduced_vector(x_full: Optional[numpy.ndarray], x_indices: Optional[List[int]] = None) → Optional[numpy.ndarray]

Keep only those elements, which indices are specified in x_indices If x_indices is not provided, delete fixed indices.

Parameters

• x_full (array_like, ndim=1) – The vector in dimension dim_full.

• x_indices – indices of x_full that should remain

property lb

property lb_init

normalize() → None

Reduce all vectors to dimension dim and have the objective accept vectors of dimension dim.

print_parameter_summary() → None

Prints a summary of what parameters are being optimized and parameter boundaries.

property ub

property ub_init

unfix_parameters(parameter_indices: Union[Iterable[SupportsInt], SupportsInt]) → None

Free specified parameters

property x_free_indices

property x_guesses

class pypesto.ProfileResult

Bases: object

Result of the profile() function.

It holds a list of profile lists. Each profile list consists of a list of ProfilerResult objects, one for each parameter.

__init__()

Initialize self. See help(type(self)) for accurate signature.

append_empty_profile_list() → int

Append an empty profile list to the list of profile lists.

Returns

The index of the created profile list.

Return type

index

append_profiler_result(profiler_result: profile.ProfilerResult = None, profile_list: int = None) → None

Append the profiler result to the profile list.

Parameters

• profiler_result – The result of one profiler run for a parameter, or None if to be left empty.

• profile_list – Index specifying the profile list to which we want to append. Defaults to the last list.

get_profiler_result(i_par: int, profile_list: Optional[int] = None)

Get theprofiler result at parameter index i_par of profile list profile_list.

Parameters

• i_par – Integer specifying the profile index.

• profile_list – Index specifying the profile list. Defaults to the last list.

set_profiler_result(profiler_result: profile.ProfilerResult, i_par: int, profile_list: int = None) → None

Write a profiler result to the result object at i_par of profile list profile_list.

Parameters

• profiler_result – The result of one (local) profiler run.

• i_par – Integer specifying the parameter index.

• profile_list – Index specifying the profile list. Defaults to the last list.

class pypesto.Result(problem=None)

Bases: object

Universal result object for pypesto. The algorithms like optimize, profile, sample fill different parts of it.

problem

The problem underlying the results.

Type

pypesto.Problem

optimize_result

The results of the optimizer runs.

profile_result

The results of the profiler run.

sample_result

The results of the sampler run.

__init__(problem=None)

Initialize self. See help(type(self)) for accurate signature.

class pypesto.SampleResult

Bases: object

Result of the sample() function.

__init__()

Initialize self. See help(type(self)) for accurate signature.

API reference

• Objective
• Problem
• Prediction
• PEtab
• Optimize
• Profile
• Sample
• Result
• Visualize
• Engines
• Startpoint
• Storage
• Logging
• Ensemble