"""Cell evaluator class"""
"""
Copyright (c) 2016-2020, EPFL/Blue Brain Project
This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>
This library is free software; you can redistribute it and/or modify it under
the terms of the GNU Lesser General Public License version 3.0 as published
by the Free Software Foundation.
This library is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
details.
You should have received a copy of the GNU Lesser General Public License
along with this library; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
"""
# pylint: disable=W0511
import logging
logger = logging.getLogger(__name__)
import bluepyopt as bpopt
import bluepyopt.tools
[docs]class CellEvaluator(bpopt.evaluators.Evaluator):
"""Simple cell class"""
def __init__(
self,
cell_model=None,
param_names=None,
fitness_protocols=None,
fitness_calculator=None,
isolate_protocols=None,
sim=None,
use_params_for_seed=False,
timeout=None):
"""Constructor
Args:
cell_model (ephys.models.CellModel): CellModel object to evaluate
param_names (list of str): names of the parameters
(parameters will be initialised in this order)
fitness_protocols (dict of str -> ephys.protocols.Protocol):
protocols used during the fitness evaluation
fitness_calculator (ObjectivesCalculator):
ObjectivesCalculator object used for the transformation of
Responses into Objective objects
isolate_protocols (bool): whether to use multiprocessing to
isolate the simulations
(disabling this could lead to unexpected behavior, and might
hinder the reproducability of the simulations)
sim (ephys.simulators.NrnSimulator): simulator to use for the cell
evaluation
use_params_for_seed (bool): use a hashed version of the parameter
dictionary as a seed for the simulator
timeout (int): duration in second after which a Process will
be interrupted when using multiprocessing
"""
super(CellEvaluator, self).__init__(
fitness_calculator.objectives,
cell_model.params_by_names(param_names))
if sim is None:
raise ValueError("CellEvaluator: you have to provide a Simulator "
"object to the 'sim' argument of the "
"CellEvaluator constructor")
self.sim = sim
self.cell_model = cell_model
self.param_names = param_names
# Stimuli used for fitness calculation
self.fitness_protocols = fitness_protocols
# Fitness value calculator
self.fitness_calculator = fitness_calculator
self.isolate_protocols = isolate_protocols
self.timeout = timeout
self.use_params_for_seed = use_params_for_seed
[docs] def param_dict(self, param_array):
"""Convert param_array in param_dict"""
param_dict = {}
for param_name, param_value in \
zip(self.param_names, param_array):
param_dict[param_name] = param_value
return param_dict
[docs] def objective_dict(self, objective_array):
"""Convert objective_array in objective_dict"""
objective_dict = {}
objective_names = [objective.name
for objective in self.fitness_calculator.objectives]
if len(objective_names) != len(objective_array):
raise Exception(
'CellEvaluator: list given to objective_dict() '
'has wrong number of objectives')
for objective_name, objective_value in \
zip(objective_names, objective_array):
objective_dict[objective_name] = objective_value
return objective_dict
[docs] def objective_list(self, objective_dict):
"""Convert objective_dict in objective_list"""
objective_list = []
objective_names = [objective.name
for objective in self.fitness_calculator.objectives]
for objective_name in objective_names:
objective_list.append(objective_dict[objective_name])
return objective_list
[docs] @staticmethod
def seed_from_param_dict(param_dict):
"""Return a seed value based on a param_dict"""
sorted_keys = sorted(param_dict.keys())
string = ''
for key in sorted_keys:
string += '%s%s' % (key, str(param_dict[key]))
return bluepyopt.tools.uint32_seed(string)
[docs] def run_protocol(
self,
protocol,
param_values,
isolate=None,
cell_model=None,
sim=None,
timeout=None):
"""Run protocol"""
sim = self.sim if sim is None else sim
if self.use_params_for_seed:
sim.random123_globalindex = self.seed_from_param_dict(param_values)
# Try/except added for backward compatibility
try:
return protocol.run(
self.cell_model if cell_model is None else cell_model,
param_values,
sim=sim,
isolate=isolate,
timeout=timeout)
except TypeError as e:
if "unexpected keyword" in str(e):
return protocol.run(
self.cell_model if cell_model is None else cell_model,
param_values,
sim=sim,
isolate=isolate)
else:
raise
[docs] def run_protocols(self, protocols, param_values):
"""Run a set of protocols"""
responses = {}
for protocol in protocols:
responses.update(self.run_protocol(
protocol,
param_values=param_values,
isolate=self.isolate_protocols,
timeout=self.timeout))
return responses
[docs] def evaluate_with_dicts(self, param_dict=None, target='scores'):
"""Run evaluation with dict as input and output"""
if target not in ['scores', 'values']:
raise Exception(
'CellEvaluator: target has to be "scores" or "values".')
if self.fitness_calculator is None:
raise Exception(
'CellEvaluator: need fitness_calculator to evaluate')
logger.debug('Evaluating %s', self.cell_model.name)
responses = self.run_protocols(
self.fitness_protocols.values(),
param_dict)
if target == 'scores':
return self.fitness_calculator.calculate_scores(responses)
elif target == 'values':
return self.fitness_calculator.calculate_values(responses)
[docs] def evaluate_with_lists(self, param_list=None, target='scores'):
"""Run evaluation with lists as input and outputs"""
param_dict = self.param_dict(param_list)
obj_dict = self.evaluate_with_dicts(
param_dict=param_dict, target=target
)
return self.objective_list(obj_dict)
[docs] def init_simulator_and_evaluate_with_lists(
self, param_list=None, target='scores'
):
"""Set NEURON variables and run evaluation with lists.
Setting the NEURON variables is necessary when using ipyparallel,
since the new subprocesses have pristine NEURON.
"""
self.sim.initialize()
return self.evaluate_with_lists(param_list=param_list, target=target)
[docs] def evaluate(self, param_list=None, target='scores'):
"""Run evaluation with lists as input and outputs"""
return self.evaluate_with_lists(param_list, target=target)
def __str__(self):
content = 'cell evaluator:\n'
content += ' cell model:\n'
if self.cell_model is not None:
content += ' %s\n' % str(self.cell_model)
content += ' fitness protocols:\n'
if self.fitness_protocols is not None:
for fitness_protocol in self.fitness_protocols.values():
content += ' %s\n' % str(fitness_protocol)
content += ' fitness calculator:\n'
if self.fitness_calculator is not None:
content += ' %s\n' % str(self.fitness_calculator)
return content