Source code for pypesto.objective.amici.amici

import abc
import copy
import os
import tempfile
from collections import OrderedDict
from pathlib import Path
from typing import TYPE_CHECKING, Dict, Optional, Sequence, Tuple, Union

import numpy as np

from ...C import (
    FVAL,
    INNER_PARAMETERS,
    MODE_FUN,
    MODE_RES,
    RDATAS,
    SUFFIXES_CSV,
    SUFFIXES_HDF5,
    ModeType,
)
from ...history import (
    CountHistory,
    CsvAmiciHistory,
    Hdf5AmiciHistory,
    HistoryOptions,
    HistoryTypeError,
    MemoryHistory,
)
from ..base import ObjectiveBase, ResultDict
from .amici_calculator import AmiciCalculator
from .amici_util import (
    create_identity_parameter_mapping,
    map_par_opt_to_par_sim,
)

if TYPE_CHECKING:
    try:
        import amici
        from amici.parameter_mapping import ParameterMapping
    except ImportError:
        pass

AmiciModel = Union['amici.Model', 'amici.ModelPtr']
AmiciSolver = Union['amici.Solver', 'amici.SolverPtr']


class AmiciObjectBuilder(abc.ABC):
    """Allows to build AMICI model, solver, and edatas.

    This class is useful for pickling an :class:`pypesto.AmiciObjective`,
    which is required in some parallelization schemes. Therefore, this
    class itself must be picklable.
    """

    @abc.abstractmethod
    def create_model(self) -> AmiciModel:
        """Create an AMICI model."""

    @abc.abstractmethod
    def create_solver(self, model: AmiciModel) -> AmiciSolver:
        """Create an AMICI solver."""

    @abc.abstractmethod
    def create_edatas(self, model: AmiciModel) -> Sequence['amici.ExpData']:
        """Create AMICI experimental data."""


[docs] class AmiciObjective(ObjectiveBase): """Allows to create an objective directly from an amici model."""
[docs] def __init__( self, amici_model: AmiciModel, amici_solver: AmiciSolver, edatas: Union[Sequence['amici.ExpData'], 'amici.ExpData'], max_sensi_order: Optional[int] = None, x_ids: Optional[Sequence[str]] = None, x_names: Optional[Sequence[str]] = None, parameter_mapping: Optional['ParameterMapping'] = None, guess_steadystate: Optional[Optional[bool]] = None, n_threads: Optional[int] = 1, fim_for_hess: Optional[bool] = True, amici_object_builder: Optional[AmiciObjectBuilder] = None, calculator: Optional[AmiciCalculator] = None, amici_reporting: Optional['amici.RDataReporting'] = None, ): """ Initialize objective. 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. amici_reporting: Determines which quantities will be computed by AMICI, see ``amici.Solver.setReturnDataReportingMode``. Set to ``None`` to compute only the minimum required information. """ import amici if amici is None: raise ImportError( "This objective requires an installation of amici " "(https://github.com/icb-dcm/amici). " "Install via `pip3 install amici`." ) self.amici_model = amici_model.clone() self.amici_solver = amici_solver.clone() # make sure the edatas are a list of edata objects if isinstance(edatas, amici.amici.ExpData): edatas = [edatas] # set the experimental data container self.edatas = edatas # set the maximum sensitivity order self.max_sensi_order = max_sensi_order self.guess_steadystate = guess_steadystate # optimization parameter ids if x_ids is None: # use model parameter ids as ids x_ids = list(self.amici_model.getParameterIds()) self.x_ids = x_ids # mapping of parameters if parameter_mapping is None: # use identity mapping for each condition parameter_mapping = create_identity_parameter_mapping( amici_model, len(edatas) ) self.parameter_mapping = parameter_mapping # If supported, enable `guess_steadystate` by default. If not # supported, disable by default. If requested but unsupported, raise. if ( self.guess_steadystate is not False and self.amici_model.nx_solver_reinit > 0 ): if self.guess_steadystate: raise ValueError( 'Steadystate prediction is not supported ' 'for models with conservation laws!' ) self.guess_steadystate = False if ( self.guess_steadystate is not False and self.amici_model.getSteadyStateSensitivityMode() == amici.SteadyStateSensitivityMode.integrationOnly ): if self.guess_steadystate: raise ValueError( 'Steadystate guesses cannot be enabled ' 'when `integrationOnly` as ' 'SteadyStateSensitivityMode!' ) self.guess_steadystate = False if self.guess_steadystate is not False: self.guess_steadystate = True if self.guess_steadystate: # preallocate guesses, construct a dict for every edata for which # we need to do preequilibration self.steadystate_guesses = { 'fval': np.inf, 'data': { iexp: {} for iexp, edata in enumerate(self.edatas) if len(edata.fixedParametersPreequilibration) }, } # optimization parameter names if x_names is None: # use ids as names x_names = x_ids self.n_threads = n_threads self.fim_for_hess = fim_for_hess self.amici_object_builder = amici_object_builder self.amici_reporting = amici_reporting if calculator is None: calculator = AmiciCalculator() self.calculator = calculator super().__init__(x_names=x_names) # Custom (condition-specific) timepoints. See the # `set_custom_timepoints` method for more information. self.custom_timepoints = None # Initialize the dictionary for saving of inner parameters. self.inner_parameters: list[float] = None
[docs] def get_config(self) -> dict: """Return basic information of the objective configuration.""" info = super().get_config() info['x_names'] = self.x_names info['model_name'] = self.amici_model.getName() info['solver'] = str(type(self.amici_solver)) info['sensi_order'] = self.max_sensi_order return info
[docs] def create_history( self, id: str, x_names: Sequence[str], options: HistoryOptions ): """See `history.generate.create_history` documentation.""" # create different history types based on the inputs if options.storage_file is None: if options.trace_record: return MemoryHistory(options=options) else: return CountHistory(options=options) # replace id template in storage file storage_file = options.storage_file.replace("{id}", id) # evaluate type suffix = Path(storage_file).suffix[1:] # create history type based on storage type if suffix in SUFFIXES_CSV: return CsvAmiciHistory( x_names=x_names, file=storage_file, options=options ) elif suffix in SUFFIXES_HDF5: return Hdf5AmiciHistory(id=id, file=storage_file, options=options) else: raise HistoryTypeError(suffix)
[docs] def initialize(self): """See `ObjectiveBase` documentation.""" super().initialize() self.reset_steadystate_guesses() self.calculator.initialize()
def __deepcopy__(self, memodict: Dict = None) -> 'AmiciObjective': import amici other = self.__class__.__new__(self.__class__) for key in set(self.__dict__.keys()) - { 'amici_model', 'amici_solver', 'edatas', }: other.__dict__[key] = copy.deepcopy(self.__dict__[key]) # copy objects that do not have __deepcopy__ other.amici_model = self.amici_model.clone() other.amici_solver = self.amici_solver.clone() other.edatas = [amici.ExpData(data) for data in self.edatas] return other def __getstate__(self) -> Dict: import amici if self.amici_object_builder is None: raise NotImplementedError( "AmiciObjective does not support __getstate__ without " "an `amici_object_builder`." ) state = {} for key in set(self.__dict__.keys()) - { 'amici_model', 'amici_solver', 'edatas', }: state[key] = self.__dict__[key] _fd, _file = tempfile.mkstemp() try: # write amici solver settings to file try: amici.writeSolverSettingsToHDF5(self.amici_solver, _file) except AttributeError as e: e.args += ( "Pickling the AmiciObjective requires an AMICI " "installation with HDF5 support.", ) raise # read in byte stream with open(_fd, 'rb', closefd=False) as f: state['amici_solver_settings'] = f.read() finally: # close file descriptor and remove temporary file os.close(_fd) os.remove(_file) state['AMICI_model_settings'] = amici.get_model_settings( self.amici_model ) return state def __setstate__(self, state: Dict) -> None: import amici if state['amici_object_builder'] is None: raise NotImplementedError( "AmiciObjective does not support __setstate__ without " "an `amici_object_builder`." ) self.__dict__.update(state) # note: attributes not defined in the builder are lost model = self.amici_object_builder.create_model() solver = self.amici_object_builder.create_solver(model) edatas = self.amici_object_builder.create_edatas(model) _fd, _file = tempfile.mkstemp() try: # write solver settings to temporary file with open(_fd, 'wb', closefd=False) as f: f.write(state['amici_solver_settings']) # read in solver settings try: amici.readSolverSettingsFromHDF5(_file, solver) except AttributeError as err: if not err.args: err.args = ('',) err.args += ( "Unpickling an AmiciObjective requires an AMICI " "installation with HDF5 support.", ) raise finally: # close file descriptor and remove temporary file os.close(_fd) os.remove(_file) self.amici_model = model self.amici_solver = solver self.edatas = edatas self.apply_custom_timepoints() amici.set_model_settings( self.amici_model, state['AMICI_model_settings'], )
[docs] def check_sensi_orders( self, sensi_orders: Tuple[int, ...], mode: ModeType, ) -> bool: """See `ObjectiveBase` documentation.""" import amici if not sensi_orders: return True sensi_order = max(sensi_orders) # dynamically obtain maximum allowed sensitivity order max_sensi_order = self.max_sensi_order if max_sensi_order is None: max_sensi_order = 1 # check whether it is ok to request 2nd order sensi_mthd = self.amici_solver.getSensitivityMethod() mthd_fwd = amici.SensitivityMethod_forward if mode == MODE_FUN and ( self.amici_model.o2mode or (sensi_mthd == mthd_fwd and self.fim_for_hess) ): max_sensi_order = 2 # evaluate sensitivity order return sensi_order <= max_sensi_order
[docs] def check_mode(self, mode: ModeType) -> bool: """See `ObjectiveBase` documentation.""" return mode in [MODE_FUN, MODE_RES]
[docs] def __call__( self, x: np.ndarray, sensi_orders: Tuple[int, ...] = (0,), mode: ModeType = MODE_FUN, return_dict: bool = False, **kwargs, ) -> Union[float, np.ndarray, Tuple, ResultDict]: """See `ObjectiveBase` documentation.""" import amici # Use AMICI full reporting if amici.ReturnDatas are returned and no # other reporting mode was set if ( return_dict and self.amici_reporting is None and 'amici_reporting' not in kwargs ): kwargs['amici_reporting'] = amici.RDataReporting.full return super().__call__(x, sensi_orders, mode, return_dict, **kwargs)
[docs] def call_unprocessed( self, x: np.ndarray, sensi_orders: Tuple[int, ...], mode: ModeType, edatas: Sequence['amici.ExpData'] = None, parameter_mapping: 'ParameterMapping' = None, amici_reporting: Optional['amici.RDataReporting'] = None, ): """ Call objective function without pre- or post-processing and formatting. Returns ------- result: A dict containing the results. """ import amici x_dct = self.par_arr_to_dct(x) # only ask amici to compute required quantities amici_reporting = ( self.amici_reporting if amici_reporting is None else amici_reporting ) if amici_reporting is None: amici_reporting = ( amici.RDataReporting.likelihood if mode == MODE_FUN else amici.RDataReporting.residuals ) self.amici_solver.setReturnDataReportingMode(amici_reporting) # update steady state if ( self.guess_steadystate and self.steadystate_guesses['fval'] < np.inf ): for data_ix in range(len(self.edatas)): self.apply_steadystate_guess(data_ix, x_dct) if edatas is None: edatas = self.edatas if parameter_mapping is None: parameter_mapping = self.parameter_mapping ret = self.calculator( x_dct=x_dct, sensi_orders=sensi_orders, mode=mode, amici_model=self.amici_model, amici_solver=self.amici_solver, edatas=edatas, n_threads=self.n_threads, x_ids=self.x_ids, parameter_mapping=parameter_mapping, fim_for_hess=self.fim_for_hess, ) nllh = ret[FVAL] rdatas = ret[RDATAS] if ret.get(INNER_PARAMETERS, None) is not None: self.inner_parameters = ret[INNER_PARAMETERS] # check whether we should update data for preequilibration guesses if ( self.guess_steadystate and nllh <= self.steadystate_guesses['fval'] and nllh < np.inf ): self.steadystate_guesses['fval'] = nllh for data_ix, rdata in enumerate(rdatas): self.store_steadystate_guess(data_ix, x_dct, rdata) return ret
[docs] def par_arr_to_dct(self, x: Sequence[float]) -> Dict[str, float]: """Create dict from parameter vector.""" return OrderedDict(zip(self.x_ids, x))
[docs] def apply_steadystate_guess(self, condition_ix: int, x_dct: Dict) -> None: """ Apply steady state guess to `edatas[condition_ix].x0`. 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)] """ mapping = self.parameter_mapping[condition_ix].map_sim_var x_sim = map_par_opt_to_par_sim(mapping, x_dct, self.amici_model) x_ss_guess = [] # resets initial state by default if condition_ix in self.steadystate_guesses['data']: guess_data = self.steadystate_guesses['data'][condition_ix] if guess_data['x_ss'] is not None: x_ss_guess = guess_data['x_ss'] if guess_data['sx_ss'] is not None: linear_update = ( guess_data['sx_ss'] .transpose() .dot( (x_sim - guess_data['x'])[ np.asarray(self.edatas[condition_ix].plist) ] ) ) # limit linear updates to max 20 % elementwise change if (linear_update / (x_ss_guess + np.spacing(1))).max() < 0.2: x_ss_guess += linear_update self.edatas[condition_ix].x0 = tuple(x_ss_guess)
[docs] def store_steadystate_guess( self, condition_ix: int, x_dct: Dict, rdata: 'amici.ReturnData', ) -> None: """ Store condition parameter, steadystate and steadystate sensitivity. Stored in steadystate_guesses if steadystate guesses are enabled for this condition. """ if condition_ix not in self.steadystate_guesses['data']: return preeq_guesses = self.steadystate_guesses['data'][condition_ix] # update parameter condition_map_sim_var = self.parameter_mapping[ condition_ix ].map_sim_var x_sim = map_par_opt_to_par_sim( condition_map_sim_var, x_dct, self.amici_model ) preeq_guesses['x'] = x_sim # update steadystates preeq_guesses['x_ss'] = rdata['x_ss'] preeq_guesses['sx_ss'] = rdata['sx_ss']
[docs] def reset_steadystate_guesses(self) -> None: """Reset all steadystate guess data.""" if not self.guess_steadystate: return self.steadystate_guesses['fval'] = np.inf for condition in self.steadystate_guesses['data']: self.steadystate_guesses['data'][condition] = {}
[docs] def apply_custom_timepoints(self) -> None: """Apply custom timepoints, if applicable. See the `set_custom_timepoints` method for more information. """ if self.custom_timepoints is not None: for index in range(len(self.edatas)): self.edatas[index].setTimepoints(self.custom_timepoints[index])
[docs] def set_custom_timepoints( self, timepoints: Sequence[Sequence[Union[float, int]]] = None, timepoints_global: Sequence[Union[float, int]] = None, ) -> 'AmiciObjective': """ Create a copy of this objective that is 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 ------- The customized copy of this objective. """ if timepoints is None and timepoints_global is None: raise KeyError('Timepoints were not specified.') amici_objective = copy.deepcopy(self) if timepoints is not None: if len(timepoints) != len(amici_objective.edatas): raise ValueError( 'The number of condition-specific timepoints `timepoints` ' 'does not match the number of experimental conditions.\n' f'Number of provided timepoints: {len(timepoints)}. ' 'Number of experimental conditions: ' f'{len(amici_objective.edatas)}.' ) custom_timepoints = timepoints else: custom_timepoints = [ copy.deepcopy(timepoints_global) for _ in range(len(amici_objective.edatas)) ] amici_objective.custom_timepoints = custom_timepoints amici_objective.apply_custom_timepoints() return amici_objective
[docs] def check_gradients_match_finite_differences( self, x: np.ndarray = None, *args, **kwargs ) -> bool: """Check if gradients match finite differences (FDs). Parameters ---------- x: The parameters for which to evaluate the gradient. Returns ------- bool Indicates whether gradients match (True) FDs or not (False) """ if x is None and 'petab_problem' in dir(self.amici_object_builder): x = self.amici_object_builder.petab_problem.x_nominal_scaled x_free = self.amici_object_builder.petab_problem.x_free_indices return super().check_gradients_match_finite_differences( *args, x=x, x_free=x_free, **kwargs )