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pylint fixes
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@whoburg
whoburg committed Jan 5, 2024
1 parent c24c7a5 commit ac4e66b
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Showing 2 changed files with 48 additions and 38 deletions.
80 changes: 42 additions & 38 deletions gpkit/constraints/gp.py
View file
Expand Up @@ -21,6 +21,7 @@


class MonoEqualityIndexes:
# pylint: disable=too-few-public-methods
"Class to hold MonoEqualityIndexes"

def __init__(self):
Expand All @@ -29,14 +30,15 @@ def __init__(self):


def _get_solver(solver, kwargs):
# pylint: disable=import-outside-toplevel
"""Get the solverfn and solvername associated with solver"""
if solver is None:
from .. import settings
try:
solver = settings["default_solver"]
except KeyError:
except KeyError as err:
raise ValueError("No default solver was set during build, so"
" solvers must be manually specified.")
" solvers must be manually specified.") from err
if solver == "cvxopt":
from ..solvers.cvxopt import optimize
elif solver == "mosek_cli":
Expand All @@ -47,7 +49,7 @@ def _get_solver(solver, kwargs):
elif hasattr(solver, "__call__"):
solver, optimize = solver.__name__, solver
else:
raise ValueError("Unknown solver '%s'." % solver)
raise ValueError(f"Unknown solver '{solver}'.")
return solver, optimize


Expand Down Expand Up @@ -117,7 +119,7 @@ def check_bounds(self, *, err_on_missing_bounds=False):
fulfill_meq_bounds(missingbounds, meq_bounds)
if missingbounds and err_on_missing_bounds:
raise UnboundedGP(
"\n\n".join("%s has no %s bound%s" % (v, b, x)
"\n\n".join(f"{v} has no {b} bound{x}"
for (v, b), x in missingbounds.items()))
return missingbounds

Expand All @@ -140,9 +142,9 @@ def gen(self):
self.meq_idxs = MonoEqualityIndexes()
m_idx = 0
row, col, data = [], [], []
for p_idx, (N_mons, hmap) in enumerate(zip(self.k, self.hmaps)):
self.p_idxs.extend([p_idx]*N_mons)
self.m_idxs.append(slice(m_idx, m_idx+N_mons))
for p_idx, (n_mons, hmap) in enumerate(zip(self.k, self.hmaps)):
self.p_idxs.extend([p_idx]*n_mons)
self.m_idxs.append(slice(m_idx, m_idx+n_mons))
if getattr(self.hmaps[p_idx], "from_meq", False):
self.meq_idxs.all.add(m_idx)
if len(self.meq_idxs.all) > 2*len(self.meq_idxs.first_half):
Expand All @@ -167,7 +169,7 @@ def gen(self):
data.extend(self.exps[i][var] for i in locs)
self.A = CootMatrix(row, col, data)

# pylint: disable=too-many-statements, too-many-locals
# pylint: disable=too-many-statements, too-many-locals, too-many-branches
def solve(self, solver=None, *, verbosity=1, gen_result=True, **kwargs):
"""Solves a GeometricProgram and returns the solution.
Expand All @@ -191,9 +193,9 @@ def solve(self, solver=None, *, verbosity=1, gen_result=True, **kwargs):
"""
solvername, solverfn = _get_solver(solver, kwargs)
if verbosity > 0:
print("Using solver '%s'" % solvername)
print(" for %i free variables" % len(self.varlocs))
print(" in %i posynomial inequalities." % len(self.k))
print(f"Using solver '{solvername}'")
print(f" for {len(self.varlocs)} free variables")
print(f" in {len(self.k)} posynomial inequalities.")

solverargs = DEFAULT_SOLVER_KWARGS.get(solvername, {})
solverargs.update(kwargs)
Expand All @@ -209,8 +211,8 @@ def solve(self, solver=None, *, verbosity=1, gen_result=True, **kwargs):
except Infeasible as e:
infeasibility = e
except InvalidLicense as e:
raise InvalidLicense("license for solver \"%s\" is invalid."
% solvername) from e
raise InvalidLicense(
f"license for solver \"{solvername}\" is invalid.") from e
except Exception as e:
raise UnknownInfeasible("Something unexpected went wrong.") from e
finally:
Expand All @@ -221,7 +223,7 @@ def solve(self, solver=None, *, verbosity=1, gen_result=True, **kwargs):
solver_out["solver"] = solvername
solver_out["soltime"] = time() - starttime
if verbosity > 0:
print("Solving took %.3g seconds." % solver_out["soltime"])
print(f"Solving took {solver_out['soltime']:.3g} seconds.")

if infeasibility:
if isinstance(infeasibility, PrimalInfeasible):
Expand Down Expand Up @@ -265,8 +267,8 @@ def generate_result(self, solver_out, *, verbosity=0, dual_check=True):
# result packing #
result = self._compile_result(solver_out) # NOTE: SIDE EFFECTS
if verbosity > 0:
print("Result packing took %.2g%% of solve time." %
((time() - tic) / soltime * 100))
respackpct = (time() - tic) / soltime * 100
print(f"Result packing took {respackpct:.2g}% of solve time.")
tic = time()
# solution checking #
initsolwarning(result, "Solution Inconsistency")
Expand All @@ -279,10 +281,10 @@ def generate_result(self, solver_out, *, verbosity=0, dual_check=True):
if not ("Dual" in msg and not dual_check):
appendsolwarning(msg, None, result, "Solution Inconsistency")
if verbosity > -4:
print("Solution check warning: %s" % msg)
print(f"Solution check warning: {msg}")
if verbosity > 0:
print("Solution checking took %.2g%% of solve time." %
((time() - tic) / soltime * 100))
solchkpct = (time() - tic) / soltime * 100
print(f"Solution checking took {solchkpct:.2g}% of solve time.")
return result

def _generate_nula(self, solver_out):
Expand Down Expand Up @@ -324,18 +326,18 @@ def _compile_result(self, solver_out):
if k in self.choicevaridxs})
result["warnings"] = {"No Dual Solution": [(\
"This model has the discretized choice variables"
" %s and hence no dual solution. You can fix those variables"
" to their optimal value and get sensitivities to the resulting"
" continuous problem by updating your model's substitions with"
" `sol[\"choicevariables\"]`."
% sorted(self.choicevaridxs.keys()), self.choicevaridxs)]}
" {sorted(self.choicevaridxs.keys())} and hence no dual "
"solution. You can fix those variables to their optimal value "
"and get sensitivities to the resulting continuous problem by "
"updating your model's substitions with "
"`sol[\"choicevariables\"]`.", self.choicevaridxs)]}
return SolutionArray(result)
if self.choicevaridxs:
result["warnings"] = {"Freed Choice Variables": [(\
"This model has the discretized choice variables"
" %s, but since the '%s' solver doesn't support discretization"
" they were treated as continuous variables."
% (sorted(self.choicevaridxs.keys()), solver_out["solver"]),
f" {sorted(self.choicevaridxs.keys())}, but since the "
f"'{solver_out['solver']}' solver doesn't support "
"discretization they were treated as continuous variables.",
self.choicevaridxs)]} # TODO: choicevaridxs seems unnecessary

result["sensitivities"] = {"constraints": {}}
Expand Down Expand Up @@ -388,6 +390,7 @@ def _compile_result(self, solver_out):
return SolutionArray(result)

def check_solution(self, cost, primal, nu, la, tol, abstol=1e-20):
# pylint: disable=too-many-arguments
"""Run checks to mathematically confirm solution solves this GP
Arguments
Expand All @@ -413,26 +416,27 @@ def almost_equal(num1, num2):
# check primal sol #
primal_exp_vals = self.cs * np.exp(A.dot(primal)) # c*e^Ax
if not almost_equal(primal_exp_vals[self.m_idxs[0]].sum(), cost):
raise Infeasible("Primal solution computed cost did not match"
" solver-returned cost: %s vs %s." %
(primal_exp_vals[self.m_idxs[0]].sum(), cost))
raise Infeasible(
"Primal solution computed cost did not match solver-returned "
f"cost: {primal_exp_vals[self.m_idxs[0]].sum()} vs {cost}.")
for mi in self.m_idxs[1:]:
if primal_exp_vals[mi].sum() > 1 + tol:
raise Infeasible("Primal solution violates constraint: %s is "
"greater than 1" % primal_exp_vals[mi].sum())
raise Infeasible("Primal solution violates constraint: "
f"{primal_exp_vals[mi].sum()} is more than 1")
# check dual sol #
if self.integersolve:
return
# note: follows dual formulation in section 3.1 of
# http://web.mit.edu/~whoburg/www/papers/hoburg_phd_thesis.pdf
if not almost_equal(self.nu_by_posy[0].sum(), 1):
objnusum = self.nu_by_posy[0].sum()
if not almost_equal(objnusum, 1):
raise Infeasible("Dual variables associated with objective sum"
" to %s, not 1" % self.nu_by_posy[0].sum())
" to {objnusum}, not 1")
if any(nu < 0):
minnu = min(nu)
if minnu < -tol/1000:
raise Infeasible("Dual solution has negative entries as"
" large as %s." % minnu)
f" large as {minnu}.")
if any(np.abs(A.T.dot(nu)) > tol):
raise Infeasible("Dual: sum of nu^T * A did not vanish.")
b = np.log(self.cs)
Expand All @@ -441,8 +445,8 @@ def almost_equal(num1, num2):
b[mi] - np.log(self.nu_by_posy[i]/la[i]))
for i, mi in enumerate(self.m_idxs) if la[i])
if not almost_equal(np.exp(dual_cost), cost):
raise Infeasible("Dual cost %s does not match primal cost %s"
% (np.exp(dual_cost), cost))
raise Infeasible(f"Dual cost {np.exp(dual_cost)} does not match "
f"primal cost {cost}")


def gen_meq_bounds(missingbounds, exps, meq_idxs): # pylint: disable=too-many-locals,too-many-branches
Expand Down Expand Up @@ -500,7 +504,7 @@ def fulfill_meq_bounds(missingbounds, meq_bounds):
still_alive = True
break
for (var, bound) in meq_bounds:
boundstr = (", but would gain it from any of these sets: ")
boundstr = ", but would gain it from any of these sets: "
for condition in list(meq_bounds[(var, bound)]):
meq_bounds[(var, bound)].remove(condition)
newcond = condition.intersection(missingbounds)
Expand Down
6 changes: 6 additions & 0 deletions pylintrc
View file
@@ -1,3 +1,9 @@
[MESSAGES CONTROL]

# Disable the message, report, category or checker with the given id(s).
disable=fixme

[BASIC]

# Good variable names which should always be accepted, separated by a comma
good-names=A

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