Source code for pypesto.objective.amici

import numpy as np
import copy
import tempfile
import os
import abc
from typing import Dict, Sequence, Union, Optional
from collections import OrderedDict

from .base import ObjectiveBase
from .constants import MODE_FUN, MODE_RES, FVAL, RDATAS
from .amici_calculator import AmiciCalculator
from .amici_util import (
    map_par_opt_to_par_sim, create_identity_parameter_mapping)

    import amici
    import amici.petab_objective
    import amici.parameter_mapping
    from amici.parameter_mapping import ParameterMapping
except ImportError:

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

[docs]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. """
[docs] @abc.abstractmethod def create_model(self) -> AmiciModel: """Create an AMICI model."""
[docs] @abc.abstractmethod def create_solver(self, model: AmiciModel) -> AmiciSolver: """Create an AMICI solver."""
[docs] @abc.abstractmethod def create_edatas(self, model: AmiciModel) -> Sequence['amici.ExpData']: """Create AMICI experimental data."""
[docs]class AmiciObjective(ObjectiveBase): """ This class 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: 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: AmiciObjectBuilder = None, 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. """ if amici is None: raise ImportError( "This objective requires an installation of 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_simulationFSA: if self.guess_steadystate: raise ValueError('Steadystate guesses cannot be enabled ' 'when `simulationFSA` 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) or self.amici_solver.getPreequilibration() } } # 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 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
[docs] def initialize(self): super().initialize() self.reset_steadystate_guesses() self.calculator.initialize()
def __deepcopy__(self, memodict: Dict = None) -> 'AmiciObjective': 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: 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'] = finally: # close file descriptor and remove temporary file os.close(_fd) os.remove(_file) return state def __setstate__(self, state: Dict): 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()
[docs] def check_sensi_orders(self, sensi_orders, mode) -> bool: 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): return mode in [MODE_FUN, MODE_RES]
[docs] def call_unprocessed(self, x, sensi_orders, mode, edatas=None, parameter_mapping=None): sensi_order = max(sensi_orders) x_dct = self.par_arr_to_dct(x) # 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_order=sensi_order, 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] # 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): """ 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']) ) # limit linear updates to max 20 % elementwise change if (x_ss_guess/linear_update).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'): """ Store condition parameter, steadystate and steadystate sensitivity 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): """Resets 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): """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 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 ------- 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