def __init__(self):
"""
Initialize a bare :class:`PlanningProblem` with all available solvers.
Arguments:
None
"""
self.solver_cvxpy = SolverCVXPY()
self.solver_pogs = SolverOptkit()
self.__solver = None
def __init__(self): """ Initialize a bare :class:`PlanningProblem` with all available solvers. Arguments: None """ self.solver_cvxpy = SolverCVXPY() self.solver_pogs = SolverOptkit() self.__solver = None
class PlanningProblem(object):
"""
Interface between :class:`~conrad.Case` and convex solvers.
Builds and solves specified treatment planning problem using fastest
available solver, then extracts solution data and solver metadata
(e.g., timing results) for use by clients of the
:class:`PlanningProblem` object (e.g., a :class:`~conrad.Case`).
Attributes:
solver_cvxpy (:class:`SolverCVXPY` or :class:`NoneType`):
:mod:`cvxpy`-baed solver, if available.
solver_pogs (:class:`SolverOptkit` or :class:`NoneType`): POGS
solver, if available.
"""
def __init__(self):
"""
Initialize a bare :class:`PlanningProblem` with all available solvers.
Arguments:
None
"""
self.solver_cvxpy = SolverCVXPY()
self.solver_pogs = SolverOptkit()
self.__solver = None
@property
def solver(self):
""" Get active solver (CVXPY or OPTKIT/POGS). """
if self.__solver is None:
if self.solver_cvxpy is not None:
return self.solver_cvxpy
elif self.solver_pogs is not None:
return self.solver_pogs
else:
raise ValueError('no solvers avaialable')
else:
return self.__solver
def __update_constraints(self, structure, slack_tol=5e-3):
"""
Pull solver results pertaining to constraint slacks.
Arguments:
structure (:class:`~conrad.medicine.Structure`): Structure
for which to retrieve constraint data.
slack_tol (:obj:`float`, optional): Numerical tolerance; if
the magnitude of the slack variable's value is smaller
than this tolerance, it is treated as zero.
Returns:
None
TODO: retrieve dual variable values.
"""
for cid in structure.constraints:
slack = self.solver.get_slack_value(cid)
if slack is not None:
# dead zone between (-slack_tol, +slack_tol) set to 0
# positive slacks get updated
# negative slacks get rejected
if slack < -slack_tol or slack > slack_tol:
structure.constraints[cid].slack = slack
def __update_structure(self, structure, exact=False):
"""
Calculate structure dose from solver's optimal beam intensities.
Arguments:
structure (:class:`~conrad.medicine.Structure`): Structure
for which to calculate dose.
exact (:obj:`bool`, optional): If ``False`` (i.e.,
reading first-pass results), trigger call to update
constraints as well.
Returns:
None
"""
structure.calc_y(self.solver.x)
if not exact:
self.__update_constraints(structure)
def __gather_solver_info(self, run_output, exact=False):
"""
Transfer solver metadata to a :class:RunOutput` instance.
Arguments:
run_output (:class:`RunOutput`):
Container for solver data. Data stored as dictionary
entries in :attr:`RunOutput.solver_info`.
exact (:obj:`bool`, optional): If ``True``, append '_exact'
to keys of dictionary entries.
Returns:
None
Raises:
TypeError: If ``run_output`` not of type :class:`RunOutput`.
"""
if not isinstance(run_output, RunOutput):
raise TypeError('Argument "run_output" must be of type {}'
''.format(RunOutput))
keymod = '_exact' if exact else ''
run_output.solver_info['status' + keymod] = self.solver.status
run_output.solver_info['time' + keymod] = self.solver.solvetime
run_output.solver_info['objective' +
keymod] = self.solver.objective_value
run_output.solver_info['iters' + keymod] = self.solver.solveiters
def __gather_solver_vars(self, run_output, exact=False):
"""
Transfer solver variables to a :class:`RunOutput` instance.
Arguments:
run_output (:class: `RunOutput`): Container for solver data.
Data stored as dictionary entries in
:attr:`RunOutput.optimal_variables`.
exact (:obj:`bool`, optional): If ``True``, append '_exact'
to keys of dictionary entries.
Returns:
None
Raises:
TypeError: If ``run_output`` not of type :class:`RunOutput`.
"""
if not isinstance(run_output, RunOutput):
raise TypeError('Argument "run_output" must be of type {}'
''.format(RunOutput))
keymod = '_exact' if exact else ''
run_output.optimal_variables['x' + keymod] = self.solver.x
run_output.optimal_variables['mu' + keymod] = self.solver.x_dual
if self.solver == self.solver_pogs:
run_output.optimal_variables['nu' + keymod] = self.solver.y_dual
else:
run_output.optimal_variables['nu' + keymod] = None
def __gather_dvh_slopes(self, run_output, structures):
"""
Transfer optimal DVH constraint slopes to :class:`RunOutput`.
For each percentile-type dose constraint in each structure in
``structures``, retrieve the optimal value of the slope variable
used in the convex restriction to the constraint.
Arguments:
run_output (:class:`RunOutput`): Container for solver data.
Data stored as dictionary entries in
:attr:`RunOutput.optimal_dvh_slopes`.
structures: Iterable collection of
:class:`~conrad.medicine.Structure` objects.
Returns:
None
Raises:
TypeError: If ``run_output`` not of type :class:`RunOutput`.
"""
if not isinstance(run_output, RunOutput):
raise TypeError('Argument "run_output" must be of type {}'
''.format(RunOutput))
# recover dvh constraint slope variables
for s in structures:
for cid in s.constraints:
run_output.optimal_dvh_slopes[cid] = self.solver.get_dvh_slope(
cid)
def __gather_constraint_slacks(self, run_output, structures):
"""
Transfer optimal dose constraint slacks to :class:`RunOutput`.
Arguments:
run_output (:class:`RunOutput`): Container for solver data.
Data stored as dictionary entries in
:attr:`RunOutput.optimal_dvh_slopes`.
structures: Iterable collection of
:class:`~conrad.medicine.Structure` objects.
Returns:
None
Raises:
TypeError: If ``run_output`` not of type :class:`RunOutput`.
"""
if not isinstance(run_output, RunOutput):
raise TypeError('Argument "run_output" must be of type {}'
''.format(RunOutput))
# recover dvh constraint slope variables
for s in structures:
for cid in s.constraints:
run_output.optimal_slacks[cid] = self.solver.get_slack_value(
cid)
def __set_solver_fastest_available(self, structures):
"""
Set active solver to fastest solver than can handle problem.
If ``structures`` includes any dose constraints, only
:mod:`cvxpy`-based solvers can be used. If no dose constraints
are present, and the module :mod:`optkit` is installed, the POGS
solver is the fastest option.
Arguments:
structures: Iterable collection of
:class:`~conrad.medicine.Structure` objects, passed to
:meth:`SolverOptkit.can_solve`.
Returns:
None
Raises:
ValueError: If neither a CVXPY solver nor an OPTKIT/POGS
solver is available.
"""
if self.solver_pogs is not None:
if self.solver_pogs.can_solve(structures):
self.__solver = self.solver_pogs
return
if self.solver_cvxpy is not None:
self.__solver = self.solver_cvxpy
return
raise ValueError('no solvers available')
def __verify_2pass_applicable(self, structures):
"""
Two-pass algorithm only needed if percentile constraints present.
Arguments:
structures: Iterable collection of
:class:`~conrad.medicine.Structure` objects to check for
percentile constraints.
Returns:
:obj:`bool`: ``True`` if any structure in ``structures`` has
a percentile dose constraint.
"""
percentile_constraints_included = False
for s in structures:
for key in s.constraints:
percentile_constraints_included |= isinstance(
s.constraints[key], PercentileConstraint)
return percentile_constraints_included
def solve(self,
structures,
run_output,
slack=True,
exact_constraints=False,
**options):
"""
Run treatment plan optimization.
Arguments:
structures: Iterable collection of
:class:`~conrad.medicine.Structure` objects with
attached objective, constraint, and dose matrix
information. Build convex model of treatment planning
problem using these data.
run_output (:class:`RunOutput`): Container for saving solver
results.
slack (:obj:`bool`, optional): If ``True``, build dose
constraints with slack.
exact_constraints (:obj:`bool`, optional): If ``True`` *and*
at least one structure has a percentile-type dose
constraint, execute the two-pass planning algorithm,
using convex restrictions of the percentile constraints
on the firstpass, and exact versions of the constraints
on the second pass.
**options: Abitrary keyword arguments, passed through to
:meth:`PlanningProblem.solver.init_problem` and
:meth:`PlanningProblem.solver.build`.
Returns:
:obj:`int`: Number of feasible solver runs performed: ``0``
if first pass infeasible, ``1`` if first pass feasible,
``2`` if two-pass method requested and both passes feasible.
Raises:
ValueError: If no solvers avaialable.
"""
if self.solver_cvxpy is None and self.solver_pogs is None:
raise ValueError('at least one of packages\n-cvxpy\n-optkit\nmust '
'be installed to perform optimization')
if 'print_construction' in options:
PRINT_PROBLEM_CONSTRUCTION = bool(options['print_construction'])
else:
PRINT_PROBLEM_CONSTRUCTION = os.getenv('CONRAD_PRINT_CONSTRUCTION',
False)
# get number of beams from dose matrix
for s in structures:
n_beams = len(s.A_mean)
break
# initialize problem with size and options
use_slack = options.pop('dvh_slack', slack)
use_2pass = options.pop('dvh_exact', exact_constraints)
use_2pass &= self.__verify_2pass_applicable(structures)
self.__set_solver_fastest_available(structures)
self.solver.init_problem(n_beams,
use_slack=use_slack,
use_2pass=use_2pass,
**options)
# build problem
construction_report = self.solver.build(structures, **options)
if PRINT_PROBLEM_CONSTRUCTION:
print('\nPROBLEM CONSTRUCTION:')
for cr in construction_report:
print(cr)
# solve
run_output.feasible = self.solver.solve(**options)
# relay output to run_output object
self.__gather_solver_info(run_output)
self.__gather_solver_vars(run_output)
self.__gather_dvh_slopes(run_output, structures)
self.__gather_constraint_slacks(run_output, structures)
run_output.solver_info['time'] = self.solver.solvetime
if not run_output.feasible:
return 0
# relay output to structures
for s in structures:
self.__update_structure(s)
# second pass, if applicable
if use_2pass and run_output.feasible:
self.solver.build(structures, exact=True)
self.solver.solve(**options)
self.__gather_solver_info(run_output, exact=True)
self.__gather_solver_vars(run_output, exact=True)
run_output.solver_info['time_exact'] = self.solver.solvetime
for s in structures:
self.__update_structure(s, exact=True)
return 2
else:
return 1
class PlanningProblem(object):
"""
Interface between :class:`~conrad.Case` and convex solvers.
Builds and solves specified treatment planning problem using fastest
available solver, then extracts solution data and solver metadata
(e.g., timing results) for use by clients of the
:class:`PlanningProblem` object (e.g., a :class:`~conrad.Case`).
Attributes:
solver_cvxpy (:class:`SolverCVXPY` or :class:`NoneType`):
:mod:`cvxpy`-baed solver, if available.
solver_pogs (:class:`SolverOptkit` or :class:`NoneType`): POGS
solver, if available.
"""
def __init__(self):
"""
Initialize a bare :class:`PlanningProblem` with all available solvers.
Arguments:
None
"""
self.solver_cvxpy = SolverCVXPY()
self.solver_pogs = SolverOptkit()
self.__solver = None
@property
def solver(self):
""" Get active solver (CVXPY or OPTKIT/POGS). """
if self.__solver is None:
if self.solver_cvxpy is not None:
return self.solver_cvxpy
elif self.solver_pogs is not None:
return self.solver_pogs
else:
raise ValueError('no solvers avaialable')
else:
return self.__solver
def __update_constraints(self, structure, slack_tol=1e-3):
"""
Pull solver results pertaining to constraint slacks.
Arguments:
structure (:class:`~conrad.medicine.Structure`): Structure
for which to retrieve constraint data.
slack_tol (:obj:`float`, optional): Numerical tolerance; if
the magnitude of the slack variable's value is smaller
than this tolerance, it is treated as zero.
Returns:
None
TODO: retrieve dual variable values.
"""
for cid in structure.constraints:
slack = self.solver.get_slack_value(cid)
if slack is not None:
# dead zone between (-slack_tol, +slack_tol) set to 0
# positive slacks get updated
# negative slacks get rejected
if slack < -slack_tol or slack > slack_tol:
structure.constraints[cid].slack = slack
def __update_structure(self, structure, exact=False):
"""
Calculate structure dose from solver's optimal beam intensities.
Arguments:
structure (:class:`~conrad.medicine.Structure`): Structure
for which to calculate dose.
exact (:obj:`bool`, optional): If ``False`` (i.e.,
reading first-pass results), trigger call to update
constraints as well.
Returns:
None
"""
structure.calc_y(self.solver.x)
if not exact:
self.__update_constraints(structure)
def __gather_solver_info(self, run_output, exact=False):
"""
Transfer solver metadata to a :class:RunOutput` instance.
Arguments:
run_output (:class:`RunOutput`):
Container for solver data. Data stored as dictionary
entries in :attr:`RunOutput.solver_info`.
exact (:obj:`bool`, optional): If ``True``, append '_exact'
to keys of dictionary entries.
Returns:
None
Raises:
TypeError: If ``run_output`` not of type :class:`RunOutput`.
"""
if not isinstance(run_output, RunOutput):
raise TypeError('Argument "run_output" must be of type {}'
''.format(RunOutput))
keymod = '_exact' if exact else ''
run_output.solver_info['status' + keymod] = self.solver.status
run_output.solver_info['time' + keymod] = self.solver.solvetime
run_output.solver_info['objective' + keymod] = self.solver.objective_value
run_output.solver_info['iters' + keymod] = self.solver.solveiters
def __gather_solver_vars(self, run_output, exact=False):
"""
Transfer solver variables to a :class:`RunOutput` instance.
Arguments:
run_output (:class: `RunOutput`): Container for solver data.
Data stored as dictionary entries in
:attr:`RunOutput.optimal_variables`.
exact (:obj:`bool`, optional): If ``True``, append '_exact'
to keys of dictionary entries.
Returns:
None
Raises:
TypeError: If ``run_output`` not of type :class:`RunOutput`.
"""
if not isinstance(run_output, RunOutput):
raise TypeError('Argument "run_output" must be of type {}'
''.format(RunOutput))
keymod = '_exact' if exact else ''
run_output.optimal_variables['x' + keymod] = self.solver.x
run_output.optimal_variables['mu' + keymod] = self.solver.x_dual
if self.solver == self.solver_pogs:
run_output.optimal_variables['nu' + keymod] = self.solver.y_dual
else:
run_output.optimal_variables['nu' + keymod] = None
def __gather_dvh_slopes(self, run_output, structures):
"""
Transfer optimal DVH constraint slopes to :class:`RunOutput`.
For each percentile-type dose constraint in each structure in
``structures``, retrieve the optimal value of the slope variable
used in the convex restriction to the constraint.
Arguments:
run_output (:class:`RunOutput`): Container for solver data.
Data stored as dictionary entries in
:attr:`RunOutput.optimal_dvh_slopes`.
structures: Iterable collection of
:class:`~conrad.medicine.Structure` objects.
Returns:
None
Raises:
TypeError: If ``run_output`` not of type :class:`RunOutput`.
"""
if not isinstance(run_output, RunOutput):
raise TypeError('Argument "run_output" must be of type {}'
''.format(RunOutput))
# recover dvh constraint slope variables
for s in structures:
for cid in s.constraints:
run_output.optimal_dvh_slopes[cid] = \
self.solver.get_dvh_slope(cid)
def __set_solver_fastest_available(self, structures):
"""
Set active solver to fastest solver than can handle problem.
If ``structures`` includes any dose constraints, only
:mod:`cvxpy`-based solvers can be used. If no dose constraints
are present, and the module :mod:`optkit` is installed, the POGS
solver is the fastest option.
Arguments:
structures: Iterable collection of
:class:`~conrad.medicine.Structure` objects, passed to
:meth:`SolverOptkit.can_solve`.
Returns:
None
Raises:
AttributeError: If neither a CVXPY solver nor an OPTKIT/POGS
solver is available.
"""
if self.solver_pogs is not None:
if self.solver_pogs.can_solve(structures):
self.__solver = self.solver_pogs
return
if self.solver_cvxpy is not None:
self.__solver = self.solver_cvxpy
return
raise AttributeError('no solvers available')
def __verify_2pass_applicable(self, structures):
"""
Two-pass algorithm only needed if percentile constraints present.
Arguments:
structures: Iterable collection of
:class:`~conrad.medicine.Structure` objects to check for
percentile constraints.
Returns:
:obj:`bool`: ``True`` if any structure in ``structures`` has
a percentile dose constraint.
"""
percentile_constraints_included = False
for s in structures:
for key in s.constraints:
percentile_constraints_included |= isinstance(
s.constraints[key], PercentileConstraint)
return percentile_constraints_included
def solve(self, structures, run_output, slack=True,
exact_constraints=False, **options):
"""
Run treatment plan optimization.
Arguments:
structures: Iterable collection of
:class:`~conrad.medicine.Structure` objects with
attached objective, constraint, and dose matrix
information. Build convex model of treatment planning
problem using these data.
run_output (:class:`RunOutput`): Container for saving solver
results.
slack (:obj:`bool`, optional): If ``True``, build dose
constraints with slack.
exact_constraints (:obj:`bool`, optional): If ``True`` *and*
at least one structure has a percentile-type dose
constraint, execute the two-pass planning algorithm,
using convex restrictions of the percentile constraints
on the firstpass, and exact versions of the constraints
on the second pass.
**options: Abitrary keyword arguments.
Returns:
:obj:`int`: Number of feasible solver runs performed: ``0``
if first pass infeasible, ``1`` if first pass feasible,
``2`` if two-pass method requested and both passes feasible.
Raises:
AttributeError: If no solvers avaialable.
"""
if self.solver_cvxpy is None and self.solver_pogs is None:
raise AttributeError('at least one of packages\n-cvxpy\n'
'-optkit\nmust be installed to perform'
' optimization')
if 'print_construction' in options:
PRINT_PROBLEM_CONSTRUCTION = bool(options['print_construction'])
else:
PRINT_PROBLEM_CONSTRUCTION = getenv('CONRAD_PRINT_CONSTRUCTION',
False)
# get number of beams from dose matrix
for s in structures:
n_beams = len(s.A_mean)
break
# initialize problem with size and options
use_slack = options.pop('dvh_slack', slack)
use_2pass = options.pop('dvh_exact', exact_constraints)
use_2pass &= self.__verify_2pass_applicable(structures)
self.__set_solver_fastest_available(structures)
self.solver.init_problem(n_beams, use_slack=use_slack,
use_2pass=use_2pass, **options)
# build problem
construction_report = self.solver.build(structures)
if PRINT_PROBLEM_CONSTRUCTION:
print('\nPROBLEM CONSTRUCTION:')
for cr in construction_report:
print(cr)
# solve
run_output.feasible = self.solver.solve(**options)
# relay output to run_output object
self.__gather_solver_info(run_output)
self.__gather_solver_vars(run_output)
self.__gather_dvh_slopes(run_output, structures)
run_output.solver_info['time'] = self.solver.solvetime
if not run_output.feasible:
return 0
# relay output to structures
for s in structures:
self.__update_structure(s)
# second pass, if applicable
if use_2pass and run_output.feasible:
self.solver.build(structures, exact=True)
self.solver.solve(**options)
self.__gather_solver_info(run_output, exact=True)
self.__gather_solver_vars(run_output, exact=True)
run_output.solver_info['time_exact'] = self.solver.solvetime
for s in structures:
self.__update_structure(s, exact=True)
return 2
else:
return 1
