def __init__(self, fun,
constr=None,
bounds=None,
eta=1e-3,
eps=1e-4,
max_iter=200):
COptimizer.__init__(self, fun=fun,
constr=constr, bounds=bounds)
# Read/write attributes
self.eta = eta # gradient step size
self.max_iter = max_iter # maximum number of iterations
self.eps = eps # tolerance value for stop criterion
def _init_solver(self):
"""Create solver instance."""
if self._solver_clf is None or self.distance is None \
or self.discrete is None:
raise ValueError('Solver not set properly!')
# map attributes to fun, constr, box
fun = CFunction(fun=self._objective_function,
gradient=self._objective_function_gradient,
n_dim=self.n_dim)
constr = CConstraint.create(self._distance)
constr.center = self._x0
constr.radius = self.dmax
# only feature increments or decrements are allowed
lb = self._x0.todense() if self.lb == 'x0' else self.lb
ub = self._x0.todense() if self.ub == 'x0' else self.ub
bounds = CConstraint.create('box', lb=lb, ub=ub)
self._solver = COptimizer.create(self._solver_type,
fun=fun,
constr=constr,
bounds=bounds,
discrete=self._discrete,
**self._solver_params)
# TODO: fix this verbose level propagation
self._solver.verbose = self.verbose
def _init_solver(self):
"""Create solver instance."""
if self._solver_clf is None or self.discrete is None:
raise ValueError('Solver not set properly!')
# map attributes to fun, constr, box
fun = CFunction(fun=self._objective_function,
gradient=self._objective_function_gradient,
n_dim=self._classifier.n_features)
bounds, constr = self._constraint_creation()
# FIXME: many solvers do now work in discrete spaces.
# this is a workaround to raise a proper error, but we should better
# handle these problems
solver_params = self.solver_params
if self.discrete is True:
solver_params['discrete'] = True
self._solver = COptimizer.create(
self._solver_type,
fun=fun, constr=constr,
bounds=bounds,
**self.solver_params)
self._solver.verbose = 0
self._warm_start = None
def __init__(self,
fun,
constr=None,
bounds=None,
eta=1e-3,
eta_min=None,
eta_max=None,
max_iter=1000,
eps=1e-4):
COptimizer.__init__(self, fun=fun, constr=constr, bounds=bounds)
# Read/write attributes
self.eta = eta
self.eta_min = eta_min
self.eta_max = eta_max
self.max_iter = max_iter
self.eps = eps
# Internal attributes
self._line_search = None
self._dtype = None
def _init_internal_solver(self, x0: CArray):
fun = CFunction(
fun=self._objective_function,
gradient=self._objective_function_gradient,
n_dim=self.n_dim,
)
constraint = CConstraint.create("l1")
constraint.center = x0
constraint.radius = 0
lb = x0.todense() if self.lb == "x0" else self.lb
ub = x0.todense() if self.ub == "x0" else self.ub
bounds = CConstraint.create("box", lb=lb, ub=ub)
self._solver = COptimizer.create(
"pgd-ls", fun=fun, constr=constraint, bounds=bounds, discrete=True
)
def _init_solver(self):
"""Create solver instance."""
if self.classifier is None:
raise ValueError('Solver not set properly!')
# map attributes to fun, constr, box
fun = CFunction(fun=self.objective_function,
gradient=self.objective_function_gradient,
n_dim=self._classifier.n_features)
bounds, constr = self._constraint_creation()
self._solver = COptimizer.create(
self._solver_type,
fun=fun, constr=constr,
bounds=bounds,
**self.solver_params)
self._solver.verbose = 0
self._warm_start = None