def test_model_composition_units(self):
class Above(Model):
"A simple upper bound on x"
def __init__(self):
x = Variable("x", "ft")
x_max = Variable("x_{max}", 1, "yard")
Model.__init__(self, 1/x, [x <= x_max])
class Below(Model):
"A simple lower bound on x"
def __init__(self):
x = Variable("x", "m")
x_min = Variable("x_{min}", 1, "cm")
Model.__init__(self, x, [x >= x_min])
a, b = Above(), Below()
concatm = Model(a.cost*b.cost, [a, b])
concat_cost = concatm.solve(verbosity=0)["cost"]
if not isinstance(a["x"].key.units, str):
self.assertAlmostEqual(a.solve(verbosity=0)["cost"], 0.3333333)
self.assertAlmostEqual(b.solve(verbosity=0)["cost"], 0.01)
self.assertAlmostEqual(concat_cost, 0.0109361) # 1 cm/1 yd
a1, b1 = Above(), Below()
m = a1.link(b1)
m.cost = m["x"]
sol = m.solve(verbosity=0)
if not isinstance(m["x"].key.units, str):
expected = (1*gpkit.units.cm/(1*m.cost.units)).to("dimensionless")
self.assertAlmostEqual(sol["cost"], expected) # 1 cm/(1 ft or 1 m)
self.assertIn(m["x"], sol["variables"])
self.assertIn(a1["x"], sol["variables"])
self.assertIn(b1["x"], sol["variables"])
self.assertNotIn(a["x"], sol["variables"])
self.assertNotIn(b["x"], sol["variables"])
def test_evalfn(self):
x = Variable("x")
x2 = Variable("x^2", evalfn=lambda solv: solv[x]**2)
m = Model(x, [x >= 2])
m.unique_varkeys = set([x2.key])
sol = m.solve(verbosity=0)
self.assertAlmostEqual(sol(x2), sol(x)**2)
def test_substitution_issue905(self):
x = Variable("x")
y = Variable("y")
m = Model(x, [x >= y], {"y": 1})
bm = Model(m.cost, Bounded(m))
sol = bm.solve(verbosity=0)
self.assertAlmostEqual(sol["cost"], 1.0)
def test_constants_in_objective_1(self):
'''Issue 296'''
x1 = Variable('x1')
x2 = Variable('x2')
m = Model(1. + x1 + x2, [x1 >= 1., x2 >= 1.])
sol = m.solve(solver=self.solver, verbosity=0)
self.assertAlmostEqual(sol["cost"], 3, self.ndig)
def __init__(self, N=4, **kwargs):
EI = Variable("EI", 1e4, "N*m^2")
dx = Variable("dx", "m", "Length of an element")
L = Variable("L", 5, "m", "Overall beam length")
q = VectorVariable(N, "q", 100*np.ones(N), "N/m",
"Distributed load at each point")
V = VectorVariable(N, "V", "N", "Internal shear")
V_tip = Variable("V_{tip}", 0, "N", "Tip loading")
M = VectorVariable(N, "M", "N*m", "Internal moment")
M_tip = Variable("M_{tip}", 0, "N*m", "Tip moment")
th = VectorVariable(N, "\\theta", "-", "Slope")
th_base = Variable("\\theta_{base}", 0, "-", "Base angle")
w = VectorVariable(N, "w", "m", "Displacement")
w_base = Variable("w_{base}", 0, "m", "Base deflection")
# below: trapezoidal integration to form a piecewise-linear
# approximation of loading, shear, and so on
# shear and moment increase from tip to base (left > right)
shear_eq = (V >= V.right + 0.5*dx*(q + q.right))
shear_eq[-1] = (V[-1] >= V_tip) # tip boundary condition
moment_eq = (M >= M.right + 0.5*dx*(V + V.right))
moment_eq[-1] = (M[-1] >= M_tip)
# slope and displacement increase from base to tip (right > left)
theta_eq = (th >= th.left + 0.5*dx*(M + M.left)/EI)
theta_eq[0] = (th[0] >= th_base) # base boundary condition
displ_eq = (w >= w.left + 0.5*dx*(th + th.left))
displ_eq[0] = (w[0] >= w_base)
# minimize tip displacement (the last w)
Model.__init__(self, w[-1],
[shear_eq, moment_eq, theta_eq, displ_eq,
L == (N-1)*dx], **kwargs)
def test_constants_in_objective_2(self):
'''Issue 296'''
x1 = Variable('x1')
x2 = Variable('x2')
m = Model(x1**2 + 100 + 3*x2, [x1 >= 10., x2 >= 15.])
sol = m.solve(solver=self.solver, verbosity=0)
self.assertAlmostEqual(sol["cost"]/245., 1, self.ndig)
def test_additive_constants(self):
x = Variable('x')
m = Model(1/x, [1 >= 5*x + 0.5, 1 >= 10*x])
m.solve(verbosity=0)
gp = m.program
self.assertEqual(gp.cs[1], gp.cs[2])
self.assertEqual(gp.A.data[1], gp.A.data[2])
def test_sp_initial_guess_sub(self):
x = Variable("x")
y = Variable("y")
x0 = 2
y0 = 2
with SignomialsEnabled():
constraints = [y + x >= 4, y <= x]
objective = x
m = Model(objective, constraints)
try:
sol = m.localsolve(x0={x: x0, y: y0}, verbosity=0,
solver=self.solver)
except TypeError:
self.fail("Call to local solve with only variables failed")
self.assertAlmostEqual(sol(x), 2, self.ndig)
self.assertAlmostEqual(sol["cost"], 2, self.ndig)
try:
sol = m.localsolve(x0={"x": x0, "y": y0}, verbosity=0,
solver=self.solver)
except TypeError:
self.fail("Call to local solve with only variable strings failed")
self.assertAlmostEqual(sol("x"), 2, self.ndig)
self.assertAlmostEqual(sol["cost"], 2, self.ndig)
try:
sol = m.localsolve(x0={"x": x0, y: y0}, verbosity=0,
solver=self.solver)
except TypeError:
self.fail("Call to local solve with a mix of variable strings "
"and variables failed")
self.assertAlmostEqual(sol["cost"], 2, self.ndig)
def __init__(self, min_rho=True, **kwargs):
self.min_rho = min_rho
Lval = 0.0065 # [K/m]
th = GRAVITATIONAL_ACCEL/(GAS_CONSTANT*Lval) # [-]
L = Variable('L', Lval, 'K/m', 'Temperature lapse rate')
p_0 = Variable('p_0', 101325, 'Pa', 'Pressure at sea level')
T_0 = Variable('T_0', 288.15, 'K', 'Temperature at sea level')
if min_rho:
objective = rho # minimize density
else:
objective = 1/rho # maximize density
# Temperature lapse rate constraint
if min_rho:
with SignomialsEnabled():
constraints = TCS([T_0 <= T + L*h])
else:
constraints = TCS([T_0 >= T + L*h])
constraints += [h <= 11000*units.m,
h >= 1E-6*units.m,
# Pressure-altitude relation
(p/p_0)**(1/th) == T/T_0,
# Ideal gas law
rho == p/(R*T),
]
su = Sutherland()
lc = su.link(constraints)
Model.__init__(self, objective, lc, **kwargs)
def test_trivial_sp2(self):
x = Variable("x")
y = Variable("y")
# converging from above
with SignomialsEnabled():
constraints = [y + x >= 2, y >= x]
objective = y
x0 = 1
y0 = 2
m = Model(objective, constraints)
sol1 = m.localsolve(x0={x: x0, y: y0}, verbosity=0, solver=self.solver)
# converging from right
with SignomialsEnabled():
constraints = [y + x >= 2, y <= x]
objective = x
x0 = 2
y0 = 1
m = Model(objective, constraints)
sol2 = m.localsolve(x0={x: x0, y: y0}, verbosity=0, solver=self.solver)
self.assertAlmostEqual(sol1["variables"]["x"],
sol2["variables"]["x"], self.ndig)
self.assertAlmostEqual(sol1["variables"]["y"],
sol2["variables"]["x"], self.ndig)
def test_partial_sub_signomial(self):
"""Test SP partial x0 initialization"""
x = Variable('x')
y = Variable('y')
with SignomialsEnabled():
m = Model(x, [x + y >= 1, y <= 0.5])
m.localsolve(x0={x: 0.5}, verbosity=0)
self.assertEqual(m.program.gps[0].constraints[0].exp[x], -1./3)
def test_cvxopt_kwargs(self):
if "cvxopt" not in settings["installed_solvers"]:
return
x = Variable("x")
m = Model(x, [x >= 12])
# make sure it's possible to pass the kktsolver option to cvxopt
sol = m.solve(solver="cvxopt", verbosity=0, kktsolver="ldl")
self.assertAlmostEqual(sol["cost"], 12., NDIGS["cvxopt"])
def test_no_naming_on_var_access(self):
# make sure that analysis models don't add their names to
# variables looked up from other models
box = Box()
area_bounds = BoxAreaBounds(box)
M = Model(box["V"], [box, area_bounds])
for var in ("h", "w", "d"):
self.assertEqual(len(M.variables_byname(var)), 1)
def test_call_units(self):
# test from issue541
x = Variable("x", 10, "ft")
y = Variable("y", "m")
m = Model(y, [y >= x])
sol = m.solve(verbosity=0)
self.assertAlmostEqual(sol("y")/sol("x"), 1.0, 6)
self.assertAlmostEqual(sol(x)/sol(y), 1.0, 6)
def test_additive_constants(self):
x = Variable('x')
m = Model(1/x, [1 >= 5*x + 0.5, 1 >= 10*x])
m.solve(verbosity=0)
# pylint: disable=no-member
gp = m.program # created by solve()
self.assertEqual(gp.cs[1], gp.cs[2])
self.assertEqual(gp.A.data[1], gp.A.data[2])
def test_sigs_not_allowed_in_cost(self):
with SignomialsEnabled():
x = Variable('x')
y = Variable('y')
J = 0.01*((x - 1)**2 + (y - 1)**2) + (x*y - 1)**2
m = Model(J)
with self.assertRaises(TypeError):
m.localsolve(verbosity=0)
def test_601(self):
# tautological monomials should solve but not pass to the solver
x = Variable("x")
y = Variable("y", 2)
m = Model(x,
[x >= 1,
y == 2])
m.solve(verbosity=0)
self.assertEqual(len(m.program.constraints), 2)
def test_partial_sub_signomial(self):
"Test SP partial x0 initialization"
x = Variable('x')
y = Variable('y')
with SignomialsEnabled():
m = Model(x, [x + y >= 1, y <= 0.5])
gp = m.sp().gp(x0={x: 0.5}) # pylint: disable=no-member
first_gp_constr_posy = gp[0][0].as_posyslt1()[0]
self.assertEqual(first_gp_constr_posy.exp[x.key], -1./3)
def simpleflight(verbosity=0):
"""Simple flight model"""
# Constants
k = Variable("k", 1.2, "-", "form factor")
e = Variable("e", 0.95, "-", "Oswald efficiency factor")
mu = Variable("\\mu", 1.78e-5, "kg/m/s", "viscosity of air")
pi = Variable("\\pi", np.pi, "-", "half of the circle constant")
rho = Variable("\\rho", 1.23, "kg/m^3", "density of air")
tau = Variable("\\tau", 0.12, "-", "airfoil thickness to chord ratio")
N_ult = Variable("N_{ult}", 3.8, "-", "ultimate load factor")
V_min = Variable("V_{min}", 22, "m/s", "takeoff speed")
C_Lmax = Variable("C_{L,max}", 1.5, "-", "max CL with flaps down")
S_wetratio = Variable("(\\frac{S}{S_{wet}})", 2.05, "-",
"wetted area ratio")
W_W_coeff1 = Variable("W_{W_{coeff1}}", 8.71e-5, "1/m",
"Wing Weight Coefficent 1")
W_W_coeff2 = Variable("W_{W_{coeff2}}", 45.24, "Pa",
"Wing Weight Coefficent 2")
CDA0 = Variable("(CDA0)", 0.031, "m^2", "fuselage drag area")
W_0 = Variable("W_0", 4940.0, "N", "aircraft weight excluding wing")
# Free Variables
D = Variable("D", "N", "total drag force")
A = Variable("A", "-", "aspect ratio")
S = Variable("S", "m^2", "total wing area")
V = Variable("V", "m/s", "cruising speed")
W = Variable("W", "N", "total aircraft weight")
Re = Variable("Re", "-", "Reynold's number")
C_D = Variable("C_D", "-", "Drag coefficient of wing")
C_L = Variable("C_L", "-", "Lift coefficent of wing")
C_f = Variable("C_f", "-", "skin friction coefficient")
W_w = Variable("W_w", "N", "wing weight")
constraints = []
# Drag model
C_D_fuse = CDA0/S
C_D_wpar = k*C_f*S_wetratio
C_D_ind = C_L**2/(pi*A*e)
constraints += [C_D >= C_D_fuse + C_D_wpar + C_D_ind]
# Wing weight model
W_w_strc = W_W_coeff1*(N_ult*A**1.5*(W_0*W*S)**0.5)/tau
W_w_surf = W_W_coeff2 * S
constraints += [W_w >= W_w_surf + W_w_strc]
# and the rest of the models
constraints += [D == 0.5*rho*S*C_D*V**2,
Re <= (rho/mu)*V*(S/A)**0.5,
C_f >= 0.074/Re**0.2,
W <= 0.5*rho*S*C_L*V**2,
W <= 0.5*rho*S*C_Lmax*V_min**2,
W >= W_0 + W_w]
m = Model(D, constraints)
m.solve(verbosity=verbosity)
return m
def test_call_vector(self):
n = 5
x = VectorVariable(n, 'x')
prob = Model(sum(x), [x >= 2.5])
sol = prob.solve(verbosity=0)
solx = sol(x)
self.assertEqual(type(solx), np.ndarray)
self.assertEqual(solx.shape, (n,))
self.assertTrue((abs(solx - 2.5*np.ones(n)) < 1e-7).all())
def test_zeroing(self):
L = Variable("L")
k = Variable("k", 0)
with SignomialsEnabled():
constr = [L-5*k <= 10]
sol = Model(1/L, constr).solve(self.solver, verbosity=0)
self.assertAlmostEqual(sol(L), 10, self.ndig)
self.assertAlmostEqual(sol["cost"], 0.1, self.ndig)
self.assertTrue(sol.almost_equal(sol))
def test_posyconstr_in_gp(self):
"""Tests tight constraint set with solve()"""
x = Variable("x")
x_min = Variable("x_{min}", 2)
m = Model(x, [Tight([x >= 1], raiseerror=True), x >= x_min])
with self.assertRaises(ValueError):
m.solve(verbosity=0)
m.substitutions[x_min] = 0.5
self.assertAlmostEqual(m.solve(verbosity=0)["cost"], 1)
def test_601(self):
# tautological monomials should solve but not pass to the solver
x = Variable("x")
y = Variable("y", 2)
m = Model(x,
[x >= 1,
y == 2])
m.solve(solver=self.solver, verbosity=0)
self.assertEqual(len(m.program[0]), 2) # pylint:disable=unsubscriptable-object
self.assertEqual(len(m.program.posynomials), 2)
def test_tautological_spconstraint(self):
x = Variable('x')
y = Variable('y')
z = Variable('z', 0)
with SignomialsEnabled():
m = Model(x, [x >= 1-y, y <= 0.1, y >= z])
with self.assertRaises(InvalidGPConstraint):
m.solve(verbosity=0)
sol = m.localsolve(self.solver, verbosity=0)
self.assertAlmostEqual(sol["variables"]["x"], 0.9, self.ndig)
def test_sub_tol(self):
""" Test PosyIneq feasibility tolerance under substitutions"""
x = Variable('x')
y = Variable('y')
z = Variable('z')
PosynomialInequality.feastol = 1e-5
m = Model(z, [x == z, x >= y], {x: 1., y: 1.0001})
self.assertRaises(ValueError, m.solve, verbosity=0)
PosynomialInequality.feastol = 1e-3
self.assertEqual(m.substitutions('x'), m.solve(verbosity=0)('x'))
def test_cost_freeing(self):
"Test freeing a variable that's in the cost."
x = Variable("x", 1)
x_min = Variable("x_{min}", 2)
m = Model(x, [x >= x_min])
self.assertRaises((RuntimeWarning, ValueError), m.solve, verbosity=0)
del m.substitutions["x"]
self.assertAlmostEqual(m.solve(verbosity=0)["cost"], 2)
del m.substitutions["x_{min}"]
self.assertRaises((RuntimeWarning, ValueError), m.solve, verbosity=0)
def test_composite_objective(self):
L = Variable("L")
W = Variable("W")
eqns = [L >= 1, W >= 1,
L*W == 10]
obj = composite_objective(L+W, W**-1 * L**-3, sub={L: 1, W: 1})
m = Model(obj, eqns)
sol = m.solve(verbosity=0)
a = sol["cost"]
b = np.array([1.58856898, 2.6410391, 3.69348122, 4.74591386])
self.assertTrue((abs(a-b)/(a+b+1e-7) < 1e-7).all())
def test_result_access(self):
x = Variable("x")
y = Variable("y")
with SignomialsEnabled():
sig = (y + 6*x >= 13 + x**2)
m = Model(y, [sig])
sol = m.localsolve(verbosity=0)
self.assertTrue(all([isinstance(gp.result.table(), Strings)
for gp in m.program.gps]))
self.assertAlmostEqual(sol["cost"]/4.0, 1.0, 5)
self.assertAlmostEqual(sol("x")/3.0, 1.0, 3)
def test_reassigned_constant_cost(self):
# for issue 1131
x = Variable('x')
x_min = Variable('x_min', 1)
y = Variable('y')
with SignomialsEnabled():
m = Model(y, [y + 0.5 >= x, x >= x_min, 6 >= y])
m.localsolve(verbosity=0, solver=self.solver)
del m.substitutions[x_min]
m.cost = 1/x_min
self.assertNotIn(x_min, m.sp().substitutions)
def test_sigeq(self):
x = Variable("x")
y = VectorVariable(1, "y") # test vector input to sigeq
c = Variable("c")
with SignomialsEnabled():
m = Model(c, [c >= (x + 0.25)**2 + (y - 0.5)**2,
SignomialEquality(x**2 + x, y)])
sol = m.localsolve(solver=self.solver, verbosity=0)
self.assertAlmostEqual(sol("x"), 0.1639472, self.ndig)
self.assertAlmostEqual(sol("y"), 0.1908254, self.ndig)
self.assertAlmostEqual(sol("c"), 0.2669448, self.ndig)
def test_issue180(self):
L = Variable("L")
Lmax = Variable("L_{max}", 10)
W = Variable("W")
Wmax = Variable("W_{max}", 10)
A = Variable("A", 10)
Obj = Variable("Obj")
a_val = 0.01
a = Variable("a", a_val)
with SignomialsEnabled():
eqns = [
L <= Lmax, W <= Wmax, L * W >= A,
Obj >= a * (2 * L + 2 * W) + (1 - a) * (12 * W**-1 * L**-3)
]
m = Model(Obj, eqns)
spsol = m.solve(self.solver, verbosity=0)
# now solve as GP
m[-1] = (Obj >= a_val * (2 * L + 2 * W) + (1 - a_val) *
(12 * W**-1 * L**-3))
del m.substitutions[m["a"]]
gpsol = m.solve(self.solver, verbosity=0)
self.assertAlmostEqual(spsol["cost"], gpsol["cost"])
def test_trivial_gp(self):
"""
Create and solve a trivial GP:
minimize x + 2y
subject to xy >= 1
The global optimum is (x, y) = (sqrt(2), 1/sqrt(2)).
"""
x = Variable('x')
y = Variable('y')
prob = Model(cost=(x + 2*y),
constraints=[x*y >= 1])
sol = prob.solve(solver=self.solver, verbosity=0)
self.assertEqual(type(prob.latex()), unicode)
# pylint: disable=protected-access
self.assertEqual(type(prob._repr_latex_()), unicode)
self.assertAlmostEqual(sol("x"), np.sqrt(2.), self.ndig)
self.assertAlmostEqual(sol("y"), 1/np.sqrt(2.), self.ndig)
self.assertAlmostEqual(sol("x") + 2*sol("y"),
2*np.sqrt(2),
self.ndig)
self.assertAlmostEqual(sol["cost"], 2*np.sqrt(2), self.ndig)
def test_posyconstr_in_sp(self):
x = Variable('x')
y = Variable('y')
with SignomialsEnabled():
sig_constraint = (x + y >= 0.1)
m = Model(x*y, [Tight([x >= y], raiseerror=True),
x >= 2, y >= 1, sig_constraint])
with self.assertRaises(ValueError):
m.localsolve(verbosity=0)
m.pop(1)
self.assertAlmostEqual(m.localsolve(verbosity=0)["cost"], 1, 5)
def rotors_analysis_function(T=2000 * ureg("lbf"),
VT="unconstrained",
h=0 * ureg.ft,
N=12,
R=1.804 * ureg("ft"),
s=0.1,
Cl_mean_max=1.4,
print_summary="No"):
#Function uses GPKit models as the backend to analyze a rotor.
testRotor = Rotors()
testRotor.substitutions.update({
"R": R,
"N": N,
"s": s,
"Cl_{mean_{max}}": Cl_mean_max
})
testState = FlightState(h=h)
testRotor_AeroAnalysis = testRotor.performance(testState)
testRotor_AeroAnalysis.substitutions.update(
{"T": T.to(ureg.lbf).magnitude})
if VT != "unconstrained":
testRotor_AeroAnalysis.substitutions.update({"VT": VT})
testModel = Model(testRotor_AeroAnalysis["P"],
[testRotor, testRotor_AeroAnalysis])
testSolution = testModel.solve(verbosity=0)
if print_summary == "Yes":
print testSolution.summary()
VT = testSolution["variables"]["VT_RotorsAero"]
P = testSolution["variables"]["P_RotorsAero"]
FOM = testSolution["variables"]["FOM_RotorsAero"]
Cl_mean = testSolution["variables"]["Cl_mean_RotorsAero"]
SPL = 20 * np.log10(testSolution["variables"]["p_{ratio}_RotorsAero"])
return [VT, P, FOM, Cl_mean, SPL]
def test_simple_united_gp(self):
R = Variable("R", "nautical_miles")
if not R.units:
return
a0 = Variable("a0", 340.29, "m/s")
theta = Variable("\\theta", 0.7598)
t = Variable("t", 10, "hr")
T_loiter = Variable("T_{loiter}", 1, "hr")
T_reserve = Variable("T_{reserve}", 45, "min")
M = VectorVariable(2, "M")
prob = Model(1 / R, [
t >= sum(R / a0 / M / theta**0.5) + T_loiter + T_reserve, M <= 0.76
])
sol = prob.solve(solver=self.solver, verbosity=0)
almostequal = self.assertAlmostEqual
almostequal(0.000553226 / R.units / sol["cost"], 1, self.ndig)
almostequal(340.29 / sol["constants"]["a0"], 1, self.ndig)
almostequal(340.29 / sol["variables"]["a0"], 1, self.ndig)
almostequal(340.29 * a0.units / sol("a0"), 1, self.ndig)
almostequal(1807.58 / sol["freevariables"]["R"], 1, self.ndig)
almostequal(1807.58 * R.units / sol("R"), 1, self.ndig)
def test_becomes_posy_sensitivities(self):
# pylint: disable=invalid-name
# model from #1165
ujet = Variable("ujet")
PK = Variable("PK")
# Constants
Dp = Variable("Dp", 0.662)
fBLI = Variable("fBLI", 0.4)
fsurf = Variable("fsurf", 0.836)
mdot = Variable("mdot", 1 / 0.7376)
with SignomialsEnabled():
m = Model(PK, [
mdot * ujet + fBLI * Dp >= 1, PK >= 0.5 * mdot * ujet *
(2 + ujet) + fBLI * fsurf * Dp
])
var_senss = m.solve(verbosity=0)["sensitivities"]["constants"]
self.assertAlmostEqual(var_senss[Dp], -0.16, 2)
self.assertAlmostEqual(var_senss[fBLI], -0.16, 2)
self.assertAlmostEqual(var_senss[fsurf], 0.19, 2)
self.assertAlmostEqual(var_senss[mdot], -0.17, 2)
def test_sp_bounded(self):
x = Variable("x")
y = Variable("y")
with SignomialsEnabled():
m = Model(x, [x + y >= 1, y <= 0.1]) # solves
cost = m.localsolve(verbosity=0)["cost"]
self.assertAlmostEqual(cost, 0.9, self.ndig)
with SignomialsEnabled():
m = Model(x, [x + y >= 1]) # dual infeasible
with self.assertRaises((RuntimeWarning, ValueError)):
m.localsolve(verbosity=0)
with SignomialsEnabled():
m = Model(x, Bounded([x + y >= 1], verbosity=0))
sol = m.localsolve(verbosity=0)
boundedness = sol["boundedness"]
if "value near lower bound" in boundedness:
self.assertIn(x.key, boundedness["value near lower bound"])
if "value near upper bound" in boundedness:
self.assertIn(y.key, boundedness["value near upper bound"])
def infeasibility_check(delta,
params_dict,
feature_names,
target_name,
patterns=None,
result_dict=None):
is_fair = (patterns is not None)
is_initialize = (result_dict is not None)
s = Variable(str("s"))
objective = s
s_constraint = [s >= 1]
constraints = get_feasibility_parity_constraints(params_dict, s,
feature_names)
constraints += s_constraint
if is_fair:
fair_constraints = get_fairness_constraints(delta, patterns,
params_dict, target_name)
#print(fair_constraints)
constraints += fair_constraints
m = Model(objective, constraints)
if is_fair:
if is_initialize:
#print("Local solver with initialization")
sol = m.localsolve(solver='mosek_cli', x0=result_dict)
else:
sol = m.localsolve(solver='mosek_cli')
else:
sol = m.solve(solver='mosek_cli')
#print("Optimal cost: %.4g" % sol["cost"])
#print(sol.summary())
result_dict_2 = {}
for key, value in params_dict.items():
result_dict_2[key] = sol["variables"][value[0]]
return result_dict_2
def test_trivial_sp(self):
x = Variable('x')
y = Variable('y')
with SignomialsEnabled():
m = Model(x, [x >= 1 - y, y <= 0.1])
with self.assertRaises(ValueError):
# solve should catch the TypeError raised by an SP constraints
# and raise its own ValueError instead
m.solve(verbosity=0)
sol = m.localsolve(self.solver, verbosity=0)
self.assertAlmostEqual(sol["variables"]["x"], 0.9, self.ndig)
with SignomialsEnabled():
m = Model(x, [x + y >= 1, y <= 0.1])
sol = m.localsolve(self.solver, verbosity=0)
self.assertAlmostEqual(sol["variables"]["x"], 0.9, self.ndig)
def optimize_aircraft(m, substitutions, fixedBPR=False, pRatOpt=True, x0 = None):
"""
Optimizes an aircraft of a given configuration
:param m: aircraft model with objective and configuration
:param fixedBPR: boolean specifying whether or not BPR is fixed (depends on config)
:param pRatOpt: boolean specifying whether or not pressure ratio is optimized (depends on config)
:return: solution of aircraft model
"""
if fixedBPR:
substitutions.update({
'\\alpha_{max}': 6.97, #8.62,
})
if pRatOpt:
del substitutions['\pi_{f_D}']
del substitutions['\pi_{lc_D}']
del substitutions['\pi_{hc_D}']
m.substitutions.update(substitutions)
m = Model(m.cost, Bounded(m), m.substitutions)
sol = m.localsolve(verbosity=2, iteration_limit=200, reltol=0.01)
return sol
def test_fmincon_generator(self):
"""Test fmincon comparison tool"""
x = Variable('x')
y = Variable('y')
m = Model(x, [x**3.2 >= 17*y + y**-0.2,
x >= 2,
y == 4])
obj, c, ceq, DC, DCeq = generate_mfiles(m, writefiles=False)
self.assertEqual(obj, 'x(2)')
self.assertEqual(c, ['-x(2)**3.2 + 17*x(1) + x(1)**-0.2', '-x(2) + 2'])
self.assertEqual(ceq, ['-x(1) + 4'])
self.assertEqual(DC, ['-0.2*x(1).^-1.2 + 17,...\n ' +
'-3.2*x(2).^2.2', '0,...\n -1'])
self.assertEqual(DCeq, ['-1,...\n 0'])
def run_M072_737(objective, fixedBPR, pRatOpt, mutategparg):
# User definitions
Nclimb = 3
Ncruise = 2
Nmission = 1
aircraft = 'M072_737'
m = Mission(Nclimb, Ncruise, objective, aircraft, Nmission)
substitutions = get_M072_737_subs()
if Nmission > 1:
substitutions.update({
'R_{req}': [3000. * units('nmi'), 2500. * units('nmi')],
'n_{pass}': [180., 180.],
})
else:
substitutions.update({
'R_{req}': 3000. * units('nmi'),
'n_{pass}': 180.,
})
if fixedBPR:
substitutions.update({
'\\alpha_{max}': 6.97,
})
if pRatOpt:
del substitutions['\pi_{f_D}']
## del substitutions['\pi_{lc_D}']
## del substitutions['\pi_{hc_D}']
m.substitutions.update(substitutions)
m = Model(m.cost, BCS(m))
m_relax = relaxed_constants(m, None, ['M_{takeoff}', '\\theta_{db}'])
sol = m_relax.localsolve(verbosity=4,
iteration_limit=200,
reltol=0.01,
mutategp=mutategparg)
post_process(sol)
percent_diff(sol, aircraft, Nclimb)
post_compute(sol, Nclimb)
return sol
def test_emp():
Sw = Variable("S_w", 50, "ft**2", "wing area")
bw = Variable("b_w", 20, "ft", "wing span")
cmac = Variable("cmac", 15, "in", "wing MAC")
emp = Empennage()
fs = FlightState()
emp.substitutions.update({emp.W: 10, emp.tailboom.l: 5,
emp.htail.planform.AR: 4,
emp.vtail.planform.AR: 4,
emp.vtail.Vv: 0.04,
emp.htail.Vh: 0.4,
emp.htail.mh: 0.01})
htperf = emp.htail.flight_model(emp.htail, fs)
vtperf = emp.vtail.flight_model(emp.vtail, fs)
tbperf = emp.tailboom.flight_model(emp.tailboom, fs)
m = Model(htperf.Cd + vtperf.Cd + tbperf.Cf,
[emp.vtail.lv == emp.tailboom.l, emp.htail.lh == emp.tailboom.l,
emp.htail.Vh <= emp.htail.planform.S*emp.htail.lh/Sw/cmac,
emp.vtail.Vv <= emp.vtail.planform.S*emp.vtail.lv/Sw/bw,
emp, fs, htperf, vtperf, tbperf])
m.solve(verbosity=0)
def relaxed_constants(model, include_only=None, exclude=None):
"""
Method to precondition an SP so it solves with a relaxed constants algorithm
ARGUMENTS
---------
model: the model to solve with relaxed constants
RETURNS
-------
feas: the input model but with relaxed constants and a new objective
"""
if model.substitutions:
constsrelaxed = ConstantsRelaxed(model, include_only, exclude)
feas = Model(constsrelaxed.relaxvars.prod()**20 * model.cost,
constsrelaxed)
# NOTE: It hasn't yet been seen but might be possible that
# the model.cost component above could cause infeasibility
else:
feas = Model(model.cost, model)
return feas
def test_posyconstr_in_gp(self):
"""Tests tight constraint set with solve()"""
x = Variable('x')
x_min = Variable('x_{min}', 2)
m = Model(x, [Tight([x >= 1], raiseerror=True), x >= x_min])
with self.assertRaises(ValueError):
m.solve(verbosity=0)
m.substitutions[x_min] = 0.5
self.assertAlmostEqual(m.solve(verbosity=0)["cost"], 1)
def test_model_composition_units(self):
class Above(Model):
"A simple upper bound on x"
def __init__(self):
x = Variable("x", "ft")
x_max = Variable("x_{max}", 1, "yard")
Model.__init__(self, 1 / x, [x <= x_max])
class Below(Model):
"A simple lower bound on x"
def __init__(self):
x = Variable("x", "m")
x_min = Variable("x_{min}", 1, "cm")
Model.__init__(self, x, [x >= x_min])
a, b = Above(), Below()
concatm = Model(a.cost * b.cost, [a, b])
concat_cost = concatm.solve(verbosity=0)["cost"]
if not isinstance(a["x"].key.units, str):
self.assertAlmostEqual(a.solve(verbosity=0)["cost"], 0.3333333)
self.assertAlmostEqual(b.solve(verbosity=0)["cost"], 0.01)
self.assertAlmostEqual(concat_cost, 0.0109361) # 1 cm/1 yd
a1, b1 = Above(), Below()
m = a1.link(b1)
m.cost = m["x"]
sol = m.solve(verbosity=0)
if not isinstance(m["x"].key.units, str):
expected = (1 * gpkit.units.cm /
(1 * m.cost.units)).to("dimensionless")
self.assertAlmostEqual(sol["cost"], expected) # 1 cm/(1 ft or 1 m)
self.assertIn(m["x"], sol["variables"])
self.assertIn(a1["x"], sol["variables"])
self.assertIn(b1["x"], sol["variables"])
self.assertNotIn(a["x"], sol["variables"])
self.assertNotIn(b["x"], sol["variables"])
def test_sp_initial_guess_sub(self):
x = Variable("x")
y = Variable("y")
x0 = 3
y0 = 2
with SignomialsEnabled():
constraints = [y + x >= 4, y <= x]
objective = x
m = Model(objective, constraints)
# Call to local solve with only variables
sol = m.localsolve(x0={
"x": x0,
y: y0
},
verbosity=0,
solver=self.solver)
self.assertAlmostEqual(sol(x), 2, self.ndig)
self.assertAlmostEqual(sol["cost"], 2, self.ndig)
# Call to local solve with only variable strings
sol = m.localsolve(x0={
"x": x0,
"y": y0
},
verbosity=0,
solver=self.solver)
self.assertAlmostEqual(sol("x"), 2, self.ndig)
self.assertAlmostEqual(sol["cost"], 2, self.ndig)
# Call to local solve with a mix of variable strings and variables
sol = m.localsolve(x0={
"x": x0,
y: y0
},
verbosity=0,
solver=self.solver)
self.assertAlmostEqual(sol["cost"], 2, self.ndig)
def test_sp_substitutions(self):
x = Variable('x')
y = Variable('y', 1)
z = Variable('z', 4)
with self.assertRaises(ValueError):
with SignomialsEnabled():
m = Model(x, [x + z >= y])
m.localsolve()
with SignomialsEnabled():
m = Model(x, [x + y >= z])
self.assertAlmostEqual(m.solve(self.solver, verbosity=0)["cost"], 3)
def synthetic_model(number_of_constraints):
constraints = []
obj = 1
number_of_gp_variables = number_of_constraints / 2 + int(
number_of_constraints * np.random.rand()) + 1
gp_variables = []
s = [] # Variable('s_relax_sm')
for i in xrange(number_of_gp_variables):
x = Variable('x_sm_%s' % i)
gp_variables.append(x)
constraints.append(x >= 0.01)
number_of_uncertain_variables = int(50 * np.random.rand()) + 1
uncertain_variables = []
for i in xrange(number_of_uncertain_variables):
uncertain_variables.append(
Variable('u_sm_%s' % i,
2 * np.random.random(),
pr=50 * np.random.random()))
for counter in xrange(number_of_constraints):
number_of_monomials = int(15 * np.random.random()) + 1
vector_to_choose_from = [0, 0, 0, 1,
-1] # , 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
m = number_of_monomials * [1]
for j in xrange(number_of_monomials):
for _ in xrange(
int(number_of_gp_variables * np.random.rand() / 2) + 1):
m[j] *= (np.random.choice(gp_variables))**(
10 * np.random.random()) # -5)
for i in xrange(number_of_uncertain_variables):
neg_pos_neutral_powers = [
vector_to_choose_from[int(
len(vector_to_choose_from) * np.random.rand())]
for _ in xrange(number_of_monomials)
]
for j in xrange(number_of_monomials):
m[j] *= uncertain_variables[i]**(np.random.rand() * 2 *
(neg_pos_neutral_powers[j]))
s.append(Variable('s_relax_sm_%s' % counter))
constraints.append(sum(m) <= s[counter])
for x in gp_variables:
obj += 1000 * np.random.rand() * x**(-np.random.rand() * 10) # - 5)
obj += sum([i**0.2 for i in s])
m = Model(obj, constraints)
return m
def test_unbounded_debugging(self):
"Test nearly-dual-feasible problems"
from gpkit.constraints.bounded import Bounded
x = Variable("x")
y = Variable("y")
m = Model(x*y, [x*y**1.000001 >= 100])
with self.assertRaises((RuntimeWarning, ValueError)):
m.solve(self.solver, verbosity=0)
m = Model(x*y, Bounded(m, verbosity=0))
sol = m.solve(self.solver, verbosity=0)
bounds = sol["boundedness"]
self.assertEqual(bounds["sensitive to upper bound"], [y.key])
self.assertEqual(bounds["sensitive to lower bound"], [x.key])
def p1(Fs, Lmax) -> float:
"""
minimize E
subject to:
L <= L_{max}
T_w >= T_w^{min}
|I^0|*E_{tx}^1 <= 1/4
var. Tw
"""
Tw = Variable('Tw')
objective = computeEnergy(Fs)(Tw)
constraints = [
computeDelay(Tw, Fs) <= Lmax, Tw >= Tw_min,
bottleneckConstraint(Fs, Tw)
]
m = Model(objective, constraints)
# m.debug() # Some problems are not feasible
try:
sol = m.solve(verbosity=0)
return round(sol['variables'][Tw], 1)
except Exception:
return None
def relaxed_constraints(model):
"""
Method to precondition an SP so it solves with a relaxed constants algorithm
ARGUMENTS
---------
model: the model to solve with relaxed constants
RETURNS
-------
feas: the input model but with relaxed constants and a new objective
"""
constsrelaxed = ConstraintsRelaxedEqually(model)
return Model(constsrelaxed.relaxvar**20 * model.cost, constsrelaxed)
def test_mdd_example(self):
Cl = Variable("Cl", 0.5, "-", "Lift Coefficient")
Mdd = Variable("Mdd", "-", "Drag Divergence Mach Number")
m1 = Model(1 / Mdd, [1 >= 5 * Mdd + 0.5, Mdd >= 0.00001])
m2 = Model(1 / Mdd, [1 >= 5 * Mdd + 0.5])
m3 = Model(1 / Mdd, [1 >= 5 * Mdd + Cl, Mdd >= 0.00001])
sol1 = m1.solve(solver=self.solver, verbosity=0)
sol2 = m2.solve(solver=self.solver, verbosity=0)
sol3 = m3.solve(solver=self.solver, verbosity=0)
# pylint: disable=no-member
gp1, gp2, gp3 = [m.program for m in [m1, m2, m3]]
self.assertEqual(
gp1.A, CootMatrix(row=[0, 1, 2], col=[0, 0, 0], data=[-1, 1, -1]))
self.assertEqual(gp2.A, CootMatrix(row=[0, 1],
col=[0, 0],
data=[-1, 1]))
self.assertEqual(
gp3.A, CootMatrix(row=[0, 1, 2], col=[0, 0, 0], data=[-1, 1, -1]))
self.assertAlmostEqual(sol1(Mdd), sol2(Mdd))
self.assertAlmostEqual(sol1(Mdd), sol3(Mdd))
self.assertAlmostEqual(sol2(Mdd), sol3(Mdd))
def test_posyconstr_in_sp(self):
x = Variable('x')
y = Variable('y')
x_min = Variable('x_min', 1)
y_min = Variable('y_min', 2)
with SignomialsEnabled():
sig_constraint = (x + y >= 3.5)
m = Model(x*y, [Loose([x >= y], raiseerror=True),
x >= x_min, y >= y_min, sig_constraint])
with self.assertRaises(ValueError):
m.localsolve(verbosity=0)
m.substitutions[x_min] = 2
m.substitutions[y_min] = 1
self.assertAlmostEqual(m.localsolve(verbosity=0)["cost"], 2.5, 5)
def test_vector_sweep(self):
"""Test sweep involving VectorVariables"""
x = Variable("x")
y = VectorVariable(2, "y")
m = Model(x, [x >= y.prod()])
m.substitutions.update({y: ('sweep', [[2, 3], [5, 7], [9, 11]])})
a = m.solve(verbosity=0)["cost"]
b = [6, 14, 22, 15, 35, 55, 27, 63, 99]
# below line fails with changing dictionary keys in py3
self.assertTrue(all(abs(a - b) / (a + b) < 1e-7))
m = Model(x, [x >= y.prod()])
m.substitutions.update({y: ('sweep', [[2, 3], [5, 7, 11]])})
a = m.solve(verbosity=0)["cost"]
b = [10, 14, 22, 15, 21, 33]
self.assertTrue(all(abs(a - b) / (a + b) < 1e-7))
m = Model(x, [x >= y.prod()])
m.substitutions.update({y: ('sweep', [[2, 3, 9], [5, 7, 11]])})
self.assertRaises(ValueError, m.solve, verbosity=0)
def test_reassigned_constant_cost(self):
# for issue 1131
x = Variable("x")
x_min = Variable("x_min", 1)
y = Variable("y")
with SignomialsEnabled():
m = Model(y, [y + 0.5 >= x, x >= x_min, 6 >= y])
m.localsolve(verbosity=0, solver=self.solver)
del m.substitutions[x_min]
m.cost = 1 / x_min
self.assertNotIn(x_min, m.sp().gp().substitutions) # pylint: disable=no-member
def test_becomes_signomial(self):
"Test that a GP does not become an SP after substitutions"
x = Variable("x")
c = Variable("c")
y = Variable("y")
m = Model(x, [y >= 1 + c * x, y <= 0.5], {c: -1})
with self.assertRaises(InvalidGPConstraint):
with SignomialsEnabled():
m.gp()
with self.assertRaises(UnnecessarySGP):
m.localsolve(solver=self.solver)
def test_trivial_sp(self):
x = Variable('x')
y = Variable('y')
with SignomialsEnabled():
m = Model(x, [x >= 1 - y, y <= 0.1])
sol = m.localsolve(verbosity=0, solver=self.solver)
self.assertAlmostEqual(sol["variables"]["x"], 0.9, self.ndig)
with SignomialsEnabled():
m = Model(x, [x + y >= 1, y <= 0.1])
sol = m.localsolve(verbosity=0, solver=self.solver)
self.assertAlmostEqual(sol["variables"]["x"], 0.9, self.ndig)
def test_reassigned_constant_cost(self):
# for issue 1131
x = Variable('x')
x_min = Variable('x_min', 1)
y = Variable('y')
with SignomialsEnabled():
m = Model(y, [y + 0.5 >= x, x >= x_min, 6 >= y])
m.localsolve(verbosity=0, solver=self.solver)
del m.substitutions[x_min]
m.cost = 1 / x_min
self.assertNotIn(x_min, m.sp().substitutions)
def test_relaxation(self):
x = Variable("x")
y = Variable("y")
with SignomialsEnabled():
constraints = [y + x >= 2, y <= x]
objective = x
m = Model(objective, constraints)
m.localsolve(verbosity=0)
# issue #257
A = VectorVariable(2, "A")
B = ArrayVariable([2, 2], "B")
C = VectorVariable(2, "C")
with SignomialsEnabled():
constraints = [A <= B.dot(C), B <= 1, C <= 1]
obj = 1 / A[0] + 1 / A[1]
m = Model(obj, constraints)
m.localsolve(verbosity=0)
