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model.py
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model.py
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"Implements Model"
import numpy as np
from .costed import CostedConstraintSet
from ..nomials import Monomial
from .prog_factories import progify, solvify
from .gp import GeometricProgram
from .sgp import SequentialGeometricProgram
from ..tools.autosweep import autosweep_1d
from ..exceptions import InvalidGPConstraint
from .. import NamedVariables
from ..tools.docstring import expected_unbounded
from .set import add_meq_bounds
from ..exceptions import Infeasible
class Model(CostedConstraintSet):
"""Symbolic representation of an optimization problem.
The Model class is used both directly to create models with constants and
sweeps, and indirectly inherited to create custom model classes.
Arguments
---------
cost : Posynomial (optional)
Defaults to `Monomial(1)`.
constraints : ConstraintSet or list of constraints (optional)
Defaults to an empty list.
substitutions : dict (optional)
This dictionary will be substituted into the problem before solving,
and also allows the declaration of sweeps and linked sweeps.
Attributes with side effects
----------------------------
`program` is set during a solve
`solution` is set at the end of a solve
"""
program = None
solution = None
def __init__(self, cost=None, constraints=None, *args, **kwargs):
setup_vars = None
substitutions = kwargs.pop("substitutions", None) # reserved keyword
if hasattr(self, "setup"):
self.cost = None
# lineage holds the (name, num) environment a model was created in,
# including its own (name, num), and those of models above it
with NamedVariables(self.__class__.__name__) as (self.lineage,
setup_vars):
args = tuple(arg for arg in [cost, constraints]
if arg is not None) + args
cs = self.setup(*args, **kwargs) # pylint: disable=no-member
if (isinstance(cs, tuple) and len(cs) == 2
and isinstance(cs[1], dict)):
constraints, substitutions = cs
else:
constraints = cs
cost = self.cost
elif args and not substitutions:
# backwards compatibility: substitutions as third argument
substitutions, = args
cost = cost or Monomial(1)
constraints = constraints or []
if setup_vars:
# add all the vars created in .setup to the Model's varkeys
# even if they aren't used in any constraints
self.unique_varkeys = frozenset(v.key for v in setup_vars)
CostedConstraintSet.__init__(self, cost, constraints, substitutions)
docstr = self.__class__.__doc__
if self.lineage and docstr and "SKIP VERIFICATION" not in docstr:
if "Unbounded" in docstr or "Bounded by" in docstr:
self.verify_docstring()
gp = progify(GeometricProgram)
solve = solvify(progify(GeometricProgram, "solve"))
sp = progify(SequentialGeometricProgram)
localsolve = solvify(progify(SequentialGeometricProgram, "localsolve"))
def verify_docstring(self): # pylint:disable=too-many-locals,too-many-branches,too-many-statements
"Verifies docstring bounds are sufficient but not excessive."
err = "while verifying %s:\n" % self.__class__.__name__
bounded, meq_bounded = self.bounded.copy(), self.meq_bounded.copy()
doc = self.__class__.__doc__
exp_unbounds = expected_unbounded(self, doc)
unexp_bounds = bounded.intersection(exp_unbounds)
if unexp_bounds: # anything bounded that shouldn't be? err!
for direction in ["lower", "upper"]:
badvks = [v for v, d in unexp_bounds if d == direction]
if not badvks:
continue
badvks = ", ".join(str(v) for v in badvks)
badvks += (" were" if len(badvks) > 1 else " was")
err += (" %s %s-bounded; expected %s-unbounded"
"\n" % (badvks, direction, direction))
raise ValueError(err)
bounded.update(exp_unbounds) # if not, treat expected as bounded
add_meq_bounds(bounded, meq_bounded) # and add more meqs
self.missingbounds = {} # now let's figure out what's missing
for bound in meq_bounded: # first add the un-dealt-with meq bounds
for condition in list(meq_bounded[bound]):
meq_bounded[bound].remove(condition)
newcond = condition - bounded
if newcond and not any(c.issubset(newcond)
for c in meq_bounded[bound]):
meq_bounded[bound].add(newcond)
bsets = " or ".join(str(list(c)) for c in meq_bounded[bound])
self.missingbounds[bound] = (", but would gain it from any of"
" these sets of bounds: " + bsets)
# then add everything that's not in bounded
if len(bounded)+len(self.missingbounds) != 2*len(self.varkeys):
for key in self.varkeys:
for bound in ("upper", "lower"):
if (key, bound) not in bounded:
if (key, bound) not in self.missingbounds:
self.missingbounds[(key, bound)] = ""
if self.missingbounds: # anything unbounded? err!
boundstrs = "\n".join(" %s has no %s bound%s" % (v, b, x)
for (v, b), x
in self.missingbounds.items())
docstring = ("To fix this add the following to %s's"
" docstring (you may not need it all):"
" \n" % self.__class__.__name__)
for direction in ["upper", "lower"]:
mb = [k for (k, b) in self.missingbounds if b == direction]
if mb:
docstring += """
%s Unbounded
---------------
%s
""" % (direction.title(), ", ".join(set(k.name for k in mb)))
raise ValueError(err + boundstrs + "\n\n" + docstring)
def sweep(self, sweeps, **solveargs):
"Sweeps {var: values} pairs in sweeps. Returns swept solutions."
sols = []
for sweepvar, sweepvals in sweeps.items():
original_val = self.substitutions.get(sweepvar, None)
self.substitutions.update({sweepvar: ('sweep', sweepvals)})
try:
sols.append(self.solve(**solveargs))
except InvalidGPConstraint:
sols.append(self.localsolve(**solveargs))
if original_val:
self.substitutions[sweepvar] = original_val
else:
del self.substitutions[sweepvar]
return sols if len(sols) > 1 else sols[0]
def autosweep(self, sweeps, tol=0.01, samplepoints=100, **solveargs):
"""Autosweeps {var: (start, end)} pairs in sweeps to tol.
Returns swept and sampled solutions.
The original simplex tree can be accessed at sol.bst
"""
sols = []
for sweepvar, sweepvals in sweeps.items():
sweepvar = self[sweepvar].key
start, end = sweepvals
bst = autosweep_1d(self, tol, sweepvar, [start, end], **solveargs)
sols.append(bst.sample_at(np.linspace(start, end, samplepoints)))
return sols if len(sols) > 1 else sols[0]
def debug(self, solver=None, verbosity=1, **solveargs):
"Attempts to diagnose infeasible models."
from .relax import ConstantsRelaxed, ConstraintsRelaxed
from .bounded import Bounded
sol = None
solveargs["solver"] = solver
solveargs["verbosity"] = verbosity - 1
solveargs["process_result"] = False
bounded = Bounded(self)
tants = ConstantsRelaxed(bounded)
if tants.relaxvars.size:
feas = Model(tants.relaxvars.prod()**30 * self.cost, tants)
else:
feas = Model(self.cost, bounded)
try:
try:
sol = feas.solve(**solveargs)
except InvalidGPConstraint:
sol = feas.sp(use_pccp=False).localsolve(**solveargs)
# limited results processing
bounded.check_boundaries(sol)
tants.check_relaxed(sol)
except Infeasible:
if verbosity:
print("<DEBUG> Model is not feasible with relaxed constants"
" and bounded variables.")
traints = ConstraintsRelaxed(self)
feas = Model(traints.relaxvars.prod()**30 * self.cost, traints)
try:
try:
sol = feas.solve(**solveargs)
except InvalidGPConstraint:
sol = feas.sp(use_pccp=False).localsolve(**solveargs)
# limited results processing
traints.check_relaxed(sol)
except Infeasible:
print("<DEBUG> Model is not feasible with bounded constraints.")
if sol and verbosity:
warnings = sol.table(tables=["warnings"]).split("\n")[3:-2]
if warnings:
print("<DEBUG> Model is feasible with these modifications:")
print("\n" + "\n".join(warnings) + "\n")
else:
print("<DEBUG> Model seems feasible without modification,"
" or only needs relaxations of less than 1%."
" Check the returned solution for details.")
return sol