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Prevent adding constraints to solved problem

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phschiele
2021-05-18 18:09:17 +02:00
parent 284e39d233
commit 108e05220c
1 changed files with 26 additions and 18 deletions
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44
pypfopt/base_optimizer.py
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@@ -7,6 +7,7 @@ Additionally, we define a general utility function ``portfolio_performance`` to
evaluate return and risk for a given set of portfolio weights.
"""
import collections
import copy
import json
import warnings
import numpy as np
@@ -174,12 +175,11 @@ class BaseConvexOptimizer(BaseOptimizer):
self._constraints = []
self._lower_bounds = None
self._upper_bounds = None
self._map_bounds_to_constraints(weight_bounds)
self._opt = None
self._solver = solver
self._verbose = verbose
self._solver_options = solver_options if solver_options else {}
self._map_bounds_to_constraints(weight_bounds)
def _map_bounds_to_constraints(self, test_bounds):
"""
@@ -218,8 +218,8 @@ class BaseConvexOptimizer(BaseOptimizer):
self._lower_bounds = np.nan_to_num(lower, nan=-1)
self._upper_bounds = np.nan_to_num(upper, nan=1)
self._constraints.append(self._w >= self._lower_bounds)
self._constraints.append(self._w <= self._upper_bounds)
self.add_constraint(lambda x: x >= self._lower_bounds)
self.add_constraint(lambda x: x <= self._upper_bounds)
def is_parameter_defined(self, parameter_name: str) -> bool:
is_defined = False
@@ -234,11 +234,14 @@ class BaseConvexOptimizer(BaseOptimizer):
def update_parameter_value(self, parameter_name: str, new_value: float) -> None:
assert self.is_parameter_defined(parameter_name)
was_updated = False
for const in self._constraints:
for arg in const.args:
if isinstance(arg, cp.Parameter):
if arg.name() == parameter_name:
arg.value = new_value
was_updated = True
assert was_updated
def _solve_cvxpy_opt_problem(self):
"""
@@ -248,14 +251,19 @@ class BaseConvexOptimizer(BaseOptimizer):
:raises exceptions.OptimizationError: if problem is not solvable by cvxpy
"""
try:
self._opt = cp.Problem(cp.Minimize(self._objective), self._constraints)
if self._solver is not None:
self._opt.solve(
solver=self._solver, verbose=self._verbose, **self._solver_options
)
if self._opt is None:
self._opt = cp.Problem(cp.Minimize(self._objective), self._constraints)
self._initial_objective = self._objective.id
self._initial_constraint_ids = {const.id for const in self._constraints}
else:
self._opt.solve(verbose=self._verbose, **self._solver_options)
assert self._objective.id == self._initial_objective, \
"The objective function was changed after the initial optimization. " \
"Please create a new instance instead."
assert {const.id for const in self._constraints} == self._initial_constraint_ids, \
"The constraints were changed after the initial optimization. " \
"Please create a new instance instead."
self._opt.solve(solver=self._solver, verbose=self._verbose, **self._solver_options)
except (TypeError, cp.DCPError) as e:
raise exceptions.OptimizationError from e
@@ -282,8 +290,8 @@ class BaseConvexOptimizer(BaseOptimizer):
:type new_objective: cp.Expression (i.e function of cp.Variable)
"""
if self._opt is not None:
warnings.warn('Adding further objectives to an already solved problem might have unintended consequences.'
'Add some further explanation... ')
raise Exception('Adding objectives to an already solved problem might have unintended consequences.'
'A new instance should be created for the new set of objectives.')
self._additional_objectives.append(new_objective(self._w, **kwargs))
def add_constraint(self, new_constraint):
@@ -303,8 +311,8 @@ class BaseConvexOptimizer(BaseOptimizer):
if not callable(new_constraint):
raise TypeError("New constraint must be provided as a lambda function")
if self._opt is not None:
warnings.warn('Adding further constraints to an already solved problem might have unintended consequences.'
'Add some further explanation... ')
raise Exception('Adding constraints to an already solved problem might have unintended consequences.'
'A new instance should be created for the new set of constraints.')
self._constraints.append(new_constraint(self._w))
def add_sector_constraints(self, sector_mapper, sector_lower, sector_upper):
@@ -341,10 +349,10 @@ class BaseConvexOptimizer(BaseOptimizer):
)
for sector in sector_upper:
is_sector = [sector_mapper[t] == sector for t in self.tickers]
self._constraints.append(cp.sum(self._w[is_sector]) <= sector_upper[sector])
self.add_constraint(lambda x: cp.sum(x[is_sector]) <= sector_upper[sector])
for sector in sector_lower:
is_sector = [sector_mapper[t] == sector for t in self.tickers]
self._constraints.append(cp.sum(self._w[is_sector]) >= sector_lower[sector])
self.add_constraint(lambda x: cp.sum(x[is_sector]) >= sector_lower[sector])
def convex_objective(self, custom_objective, weights_sum_to_one=True, **kwargs):
"""
@@ -374,7 +382,7 @@ class BaseConvexOptimizer(BaseOptimizer):
self._objective += obj
if weights_sum_to_one:
self._constraints.append(cp.sum(self._w) == 1)
self.add_constraint(lambda x: cp.sum(x) == 1)
return self._solve_cvxpy_opt_problem()