/
t_primitives.py
127 lines (117 loc) · 6.18 KB
/
t_primitives.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
import numpy as np
import os
import pickle
import unittest
from gpkit.tests.helpers import run_tests
from gpkit import Variable, Model
from robust.robust_gp_tools import RobustGPTools
from robust.twoterm_approximation import TwoTermApproximation
from robust.robust import RobustModel
from robust.margin import MarginModel
from robust.testing.models import simple_wing
from robust.testing.models import sp_test_model, gp_test_model
class TestPrimitives(unittest.TestCase):
def test_MarginModel(self):
""" Tests creation and solution of MarginModel"""
m = sp_test_model()
mm = MarginModel(m, gamma=0.5)
margin_solution = mm.localsolve(verbosity=0)
uncertain_varkeys = [k for k in m.varkeys if RobustGPTools.is_directly_uncertain(k)]
# Checking margin allocation
for key in list(uncertain_varkeys):
assert(mm.substitutions.get(key) == m.substitutions.get(key) *
(1.+mm.setting.get("gamma")*key.pr/100.))
self.assertGreater(margin_solution['cost'], mm.nominal_cost)
def test_GoalProgram(self):
""" Tests creation and solution of RobustModels with variable Gamma,
and tightness of the two solution methods."""
m = sp_test_model()
n = 6
gammas = np.linspace(0.,1.,n)
Gamma = Variable('\\Gamma', '-', 'Uncertainty bound')
solBound = Variable('1+\\delta', '-', 'Acceptable optimal solution bound', fix = True)
nominal_cost = m.localsolve(verbosity=0)['cost']
box_cost = [RobustModel(m, 'box', gamma = gammas[i]).robustsolve(verbosity=0)['cost']
for i in range(n)]/(np.ones(n)*nominal_cost)
ell_cost = [RobustModel(m, 'elliptical', gamma = gammas[i]).robustsolve(verbosity=0)['cost']
for i in range(n)]/(np.ones(n)*nominal_cost)
self.assertTrue(all(box_cost >= nominal_cost))
self.assertTrue(all(ell_cost >= nominal_cost))
# Creating goal model
gm = Model(1 / Gamma, [m, m.cost <= nominal_cost * solBound, Gamma <= 1e30, solBound <= 1e30],
m.substitutions)
goal_box_gamma = []
goal_ell_gamma = []
for i in range(1,n):
gm.substitutions.update({'1+\\delta': box_cost[i]})
robust_goal_bm = RobustModel(gm, 'box', gamma=Gamma)
goal_box_gamma.append(robust_goal_bm.robustsolve(verbosity=0)['cost']**-1)
gm.substitutions.update({'1+\\delta': ell_cost[i]})
robust_goal_em = RobustModel(gm, 'elliptical', gamma=Gamma)
goal_ell_gamma.append(robust_goal_em.robustsolve(verbosity=0)['cost']**-1)
self.assertAlmostEqual(goal_box_gamma[i-1], gammas[i], places=5)
self.assertAlmostEqual(goal_ell_gamma[i-1], gammas[i], places=5)
def test_conservativeness(self):
""" Testing conservativeness of solution methods"""
m = gp_test_model()
sm = m.solve(verbosity=0)
sem = RobustModel(m, 'elliptical').robustsolve(verbosity=0)
smm = MarginModel(m).solve(verbosity=0)
sbm = RobustModel(m, 'box').robustsolve(verbosity=0)
self.assertTrue(sm['cost'] <= sem['cost'] <= smm['cost'] <= sbm['cost'])
# def test_robustify_monomial(self):
# """ Testing whether monomials are robustified correctly"""
# m = gp_test_model()
# monys = []
# for c in m.flat(constraintsets=False):
# for monomial in c.as_posyslt1()[0].chop():
# monys.append(monomial)
# uncertain_vars = [i for i in m.varkeys if RobustGPTools.is_directly_uncertain(i)]
# rm = RobustModel(m, 'box')
# robust_monys = [rm.robustify_monomial(mony) for mony in monys]
#
# def test_two_term_tolerance(self):
# m = gp_test_model()
# rm = RobustModel(m, 'box')
# posy = [c for c in m.flat(constraintsets=False)][1].left
# tta = TwoTermApproximation(posy, rm.setting)
# data_constr = []
# no_data_constr = []
# for i,v in enumerate(tta.list_of_permutations):
# ndc, dc = tta.equivalent_posynomial(posy, i, v, False)
# no_data_constr.append(ndc)
# data_constr.append(dc)
#
# rm.calculate_value_of_two_term_approximated_posynomial(two_term_approximation, index_of_permutation,
# solution)
def test_methods(self):
m = gp_test_model()
nominal_solution = m.solve(verbosity=0)
methods = [{'name': 'BestPairs', 'twoTerm': True, 'boyd': False, 'simpleModel': False},
{'name': 'LinearizedPerturbations', 'twoTerm': False, 'boyd': False, 'simpleModel': False},
{'name': 'SimpleConservative', 'twoTerm': False, 'boyd': False, 'simpleModel': True},
{'name': 'TwoTerm', 'twoTerm': False, 'boyd': True, 'simpleModel': False}
]
uncertainty_sets = ['box', 'elliptical']
gamma = 0.5
for method in methods:
for uncertainty_set in uncertainty_sets:
rm = RobustModel(m, uncertainty_set, gamma=gamma, twoTerm=method['twoTerm'],
boyd=method['boyd'], simpleModel=method['simpleModel'],
nominalsolve=nominal_solution)
sol = rm.robustsolve(verbosity=0)
# sol.save(os.path.dirname(__file__) +
# 'diffs/test_methods/' +
# method['name'] + '_' + uncertainty_set)
# self.assertTrue(sol.almost_equal(pickle.load(open(os.path.dirname(__file__) +
# '/diffs/test_methods/' +
# method['name'] + '_' + uncertainty_set)),
# reltol=1e-2, sens_abstol=1e-2))
print(sol.diff(pickle.load(open(os.path.dirname(__file__) +
'/diffs/test_methods/' +
method['name'] + '_' + uncertainty_set))))
TESTS = [TestPrimitives]
def test():
run_tests(TESTS)
if __name__ == "__main__":
test()