Fixed database typo and removed unnecessary class identifier.
This commit is contained in:
parent
00ad49a143
commit
45fb349a7d
5098 changed files with 952558 additions and 85 deletions
|
|
@ -0,0 +1,267 @@
|
|||
"""
|
||||
Unit test for constraint conversion
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import (assert_array_almost_equal,
|
||||
assert_allclose, assert_warns, suppress_warnings)
|
||||
import pytest
|
||||
from scipy.optimize import (NonlinearConstraint, LinearConstraint,
|
||||
OptimizeWarning, minimize, BFGS)
|
||||
from .test_minimize_constrained import (Maratos, HyperbolicIneq, Rosenbrock,
|
||||
IneqRosenbrock, EqIneqRosenbrock,
|
||||
BoundedRosenbrock, Elec)
|
||||
|
||||
|
||||
class TestOldToNew(object):
|
||||
x0 = (2, 0)
|
||||
bnds = ((0, None), (0, None))
|
||||
method = "trust-constr"
|
||||
|
||||
def test_constraint_dictionary_1(self):
|
||||
fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2
|
||||
cons = ({'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2},
|
||||
{'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},
|
||||
{'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})
|
||||
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning, "delta_grad == 0.0")
|
||||
res = minimize(fun, self.x0, method=self.method,
|
||||
bounds=self.bnds, constraints=cons)
|
||||
assert_allclose(res.x, [1.4, 1.7], rtol=1e-4)
|
||||
assert_allclose(res.fun, 0.8, rtol=1e-4)
|
||||
|
||||
def test_constraint_dictionary_2(self):
|
||||
fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2
|
||||
cons = {'type': 'eq',
|
||||
'fun': lambda x, p1, p2: p1*x[0] - p2*x[1],
|
||||
'args': (1, 1.1),
|
||||
'jac': lambda x, p1, p2: np.array([[p1, -p2]])}
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning, "delta_grad == 0.0")
|
||||
res = minimize(fun, self.x0, method=self.method,
|
||||
bounds=self.bnds, constraints=cons)
|
||||
assert_allclose(res.x, [1.7918552, 1.62895927])
|
||||
assert_allclose(res.fun, 1.3857466063348418)
|
||||
|
||||
def test_constraint_dictionary_3(self):
|
||||
fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2
|
||||
cons = [{'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2},
|
||||
NonlinearConstraint(lambda x: x[0] - x[1], 0, 0)]
|
||||
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning, "delta_grad == 0.0")
|
||||
res = minimize(fun, self.x0, method=self.method,
|
||||
bounds=self.bnds, constraints=cons)
|
||||
assert_allclose(res.x, [1.75, 1.75], rtol=1e-4)
|
||||
assert_allclose(res.fun, 1.125, rtol=1e-4)
|
||||
|
||||
|
||||
class TestNewToOld(object):
|
||||
|
||||
def test_multiple_constraint_objects(self):
|
||||
fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 0.75)**2
|
||||
x0 = [2, 0, 1]
|
||||
coni = [] # only inequality constraints (can use cobyla)
|
||||
methods = ["slsqp", "cobyla", "trust-constr"]
|
||||
|
||||
# mixed old and new
|
||||
coni.append([{'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2},
|
||||
NonlinearConstraint(lambda x: x[0] - x[1], -1, 1)])
|
||||
|
||||
coni.append([LinearConstraint([1, -2, 0], -2, np.inf),
|
||||
NonlinearConstraint(lambda x: x[0] - x[1], -1, 1)])
|
||||
|
||||
coni.append([NonlinearConstraint(lambda x: x[0] - 2 * x[1] + 2, 0, np.inf),
|
||||
NonlinearConstraint(lambda x: x[0] - x[1], -1, 1)])
|
||||
|
||||
for con in coni:
|
||||
funs = {}
|
||||
for method in methods:
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning)
|
||||
result = minimize(fun, x0, method=method, constraints=con)
|
||||
funs[method] = result.fun
|
||||
assert_allclose(funs['slsqp'], funs['trust-constr'], rtol=1e-4)
|
||||
assert_allclose(funs['cobyla'], funs['trust-constr'], rtol=1e-4)
|
||||
|
||||
def test_individual_constraint_objects(self):
|
||||
fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 0.75)**2
|
||||
x0 = [2, 0, 1]
|
||||
|
||||
cone = [] # with equality constraints (can't use cobyla)
|
||||
coni = [] # only inequality constraints (can use cobyla)
|
||||
methods = ["slsqp", "cobyla", "trust-constr"]
|
||||
|
||||
# nonstandard data types for constraint equality bounds
|
||||
cone.append(NonlinearConstraint(lambda x: x[0] - x[1], 1, 1))
|
||||
cone.append(NonlinearConstraint(lambda x: x[0] - x[1], [1.21], [1.21]))
|
||||
cone.append(NonlinearConstraint(lambda x: x[0] - x[1],
|
||||
1.21, np.array([1.21])))
|
||||
|
||||
# multiple equalities
|
||||
cone.append(NonlinearConstraint(
|
||||
lambda x: [x[0] - x[1], x[1] - x[2]],
|
||||
1.21, 1.21)) # two same equalities
|
||||
cone.append(NonlinearConstraint(
|
||||
lambda x: [x[0] - x[1], x[1] - x[2]],
|
||||
[1.21, 1.4], [1.21, 1.4])) # two different equalities
|
||||
cone.append(NonlinearConstraint(
|
||||
lambda x: [x[0] - x[1], x[1] - x[2]],
|
||||
[1.21, 1.21], 1.21)) # equality specified two ways
|
||||
cone.append(NonlinearConstraint(
|
||||
lambda x: [x[0] - x[1], x[1] - x[2]],
|
||||
[1.21, -np.inf], [1.21, np.inf])) # equality + unbounded
|
||||
|
||||
# nonstandard data types for constraint inequality bounds
|
||||
coni.append(NonlinearConstraint(lambda x: x[0] - x[1], 1.21, np.inf))
|
||||
coni.append(NonlinearConstraint(lambda x: x[0] - x[1], [1.21], np.inf))
|
||||
coni.append(NonlinearConstraint(lambda x: x[0] - x[1],
|
||||
1.21, np.array([np.inf])))
|
||||
coni.append(NonlinearConstraint(lambda x: x[0] - x[1], -np.inf, -3))
|
||||
coni.append(NonlinearConstraint(lambda x: x[0] - x[1],
|
||||
np.array(-np.inf), -3))
|
||||
|
||||
# multiple inequalities/equalities
|
||||
coni.append(NonlinearConstraint(
|
||||
lambda x: [x[0] - x[1], x[1] - x[2]],
|
||||
1.21, np.inf)) # two same inequalities
|
||||
cone.append(NonlinearConstraint(
|
||||
lambda x: [x[0] - x[1], x[1] - x[2]],
|
||||
[1.21, -np.inf], [1.21, 1.4])) # mixed equality/inequality
|
||||
coni.append(NonlinearConstraint(
|
||||
lambda x: [x[0] - x[1], x[1] - x[2]],
|
||||
[1.1, .8], [1.2, 1.4])) # bounded above and below
|
||||
coni.append(NonlinearConstraint(
|
||||
lambda x: [x[0] - x[1], x[1] - x[2]],
|
||||
[-1.2, -1.4], [-1.1, -.8])) # - bounded above and below
|
||||
|
||||
# quick check of LinearConstraint class (very little new code to test)
|
||||
cone.append(LinearConstraint([1, -1, 0], 1.21, 1.21))
|
||||
cone.append(LinearConstraint([[1, -1, 0], [0, 1, -1]], 1.21, 1.21))
|
||||
cone.append(LinearConstraint([[1, -1, 0], [0, 1, -1]],
|
||||
[1.21, -np.inf], [1.21, 1.4]))
|
||||
|
||||
for con in coni:
|
||||
funs = {}
|
||||
for method in methods:
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning)
|
||||
result = minimize(fun, x0, method=method, constraints=con)
|
||||
funs[method] = result.fun
|
||||
assert_allclose(funs['slsqp'], funs['trust-constr'], rtol=1e-3)
|
||||
assert_allclose(funs['cobyla'], funs['trust-constr'], rtol=1e-3)
|
||||
|
||||
for con in cone:
|
||||
funs = {}
|
||||
for method in methods[::2]: # skip cobyla
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning)
|
||||
result = minimize(fun, x0, method=method, constraints=con)
|
||||
funs[method] = result.fun
|
||||
assert_allclose(funs['slsqp'], funs['trust-constr'], rtol=1e-3)
|
||||
|
||||
|
||||
class TestNewToOldSLSQP(object):
|
||||
method = 'slsqp'
|
||||
elec = Elec(n_electrons=2)
|
||||
elec.x_opt = np.array([-0.58438468, 0.58438466, 0.73597047,
|
||||
-0.73597044, 0.34180668, -0.34180667])
|
||||
brock = BoundedRosenbrock()
|
||||
brock.x_opt = [0, 0]
|
||||
list_of_problems = [Maratos(),
|
||||
HyperbolicIneq(),
|
||||
Rosenbrock(),
|
||||
IneqRosenbrock(),
|
||||
EqIneqRosenbrock(),
|
||||
elec,
|
||||
brock
|
||||
]
|
||||
|
||||
def test_list_of_problems(self):
|
||||
|
||||
for prob in self.list_of_problems:
|
||||
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning)
|
||||
result = minimize(prob.fun, prob.x0,
|
||||
method=self.method,
|
||||
bounds=prob.bounds,
|
||||
constraints=prob.constr)
|
||||
|
||||
assert_array_almost_equal(result.x, prob.x_opt, decimal=3)
|
||||
|
||||
def test_warn_mixed_constraints(self):
|
||||
# warns about inefficiency of mixed equality/inequality constraints
|
||||
fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 0.75)**2
|
||||
cons = NonlinearConstraint(lambda x: [x[0]**2 - x[1], x[1] - x[2]],
|
||||
[1.1, .8], [1.1, 1.4])
|
||||
bnds = ((0, None), (0, None), (0, None))
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning, "delta_grad == 0.0")
|
||||
assert_warns(OptimizeWarning, minimize, fun, (2, 0, 1),
|
||||
method=self.method, bounds=bnds, constraints=cons)
|
||||
|
||||
def test_warn_ignored_options(self):
|
||||
# warns about constraint options being ignored
|
||||
fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2 + (x[2] - 0.75)**2
|
||||
x0 = (2, 0, 1)
|
||||
|
||||
if self.method == "slsqp":
|
||||
bnds = ((0, None), (0, None), (0, None))
|
||||
else:
|
||||
bnds = None
|
||||
|
||||
cons = NonlinearConstraint(lambda x: x[0], 2, np.inf)
|
||||
res = minimize(fun, x0, method=self.method,
|
||||
bounds=bnds, constraints=cons)
|
||||
# no warnings without constraint options
|
||||
assert_allclose(res.fun, 1)
|
||||
|
||||
cons = LinearConstraint([1, 0, 0], 2, np.inf)
|
||||
res = minimize(fun, x0, method=self.method,
|
||||
bounds=bnds, constraints=cons)
|
||||
# no warnings without constraint options
|
||||
assert_allclose(res.fun, 1)
|
||||
|
||||
cons = []
|
||||
cons.append(NonlinearConstraint(lambda x: x[0]**2, 2, np.inf,
|
||||
keep_feasible=True))
|
||||
cons.append(NonlinearConstraint(lambda x: x[0]**2, 2, np.inf,
|
||||
hess=BFGS()))
|
||||
cons.append(NonlinearConstraint(lambda x: x[0]**2, 2, np.inf,
|
||||
finite_diff_jac_sparsity=42))
|
||||
cons.append(NonlinearConstraint(lambda x: x[0]**2, 2, np.inf,
|
||||
finite_diff_rel_step=42))
|
||||
cons.append(LinearConstraint([1, 0, 0], 2, np.inf,
|
||||
keep_feasible=True))
|
||||
for con in cons:
|
||||
assert_warns(OptimizeWarning, minimize, fun, x0,
|
||||
method=self.method, bounds=bnds, constraints=cons)
|
||||
|
||||
|
||||
class TestNewToOldCobyla(object):
|
||||
method = 'cobyla'
|
||||
|
||||
list_of_problems = [
|
||||
Elec(n_electrons=2),
|
||||
Elec(n_electrons=4),
|
||||
]
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_list_of_problems(self):
|
||||
|
||||
for prob in self.list_of_problems:
|
||||
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning)
|
||||
truth = minimize(prob.fun, prob.x0,
|
||||
method='trust-constr',
|
||||
bounds=prob.bounds,
|
||||
constraints=prob.constr)
|
||||
result = minimize(prob.fun, prob.x0,
|
||||
method=self.method,
|
||||
bounds=prob.bounds,
|
||||
constraints=prob.constr)
|
||||
|
||||
assert_allclose(result.fun, truth.fun, rtol=1e-3)
|
||||
Loading…
Add table
Add a link
Reference in a new issue