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762 lines
26 KiB
Python
762 lines
26 KiB
Python
import logging
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import numpy
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import pytest
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from pytest import raises as assert_raises, warns
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from scipy.optimize import shgo
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from scipy.optimize._shgo import SHGO
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class StructTestFunction(object):
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def __init__(self, bounds, expected_x, expected_fun=None,
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expected_xl=None, expected_funl=None):
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self.bounds = bounds
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self.expected_x = expected_x
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self.expected_fun = expected_fun
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self.expected_xl = expected_xl
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self.expected_funl = expected_funl
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def wrap_constraints(g):
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cons = []
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if g is not None:
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if (type(g) is not tuple) and (type(g) is not list):
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g = (g,)
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else:
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pass
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for g in g:
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cons.append({'type': 'ineq',
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'fun': g})
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cons = tuple(cons)
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else:
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cons = None
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return cons
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class StructTest1(StructTestFunction):
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def f(self, x):
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return x[0] ** 2 + x[1] ** 2
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def g(x):
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return -(numpy.sum(x, axis=0) - 6.0)
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cons = wrap_constraints(g)
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test1_1 = StructTest1(bounds=[(-1, 6), (-1, 6)],
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expected_x=[0, 0])
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test1_2 = StructTest1(bounds=[(0, 1), (0, 1)],
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expected_x=[0, 0])
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test1_3 = StructTest1(bounds=[(None, None), (None, None)],
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expected_x=[0, 0])
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class StructTest2(StructTestFunction):
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"""
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Scalar function with several minima to test all minimizer retrievals
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"""
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def f(self, x):
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return (x - 30) * numpy.sin(x)
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def g(x):
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return 58 - numpy.sum(x, axis=0)
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cons = wrap_constraints(g)
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test2_1 = StructTest2(bounds=[(0, 60)],
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expected_x=[1.53567906],
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expected_fun=-28.44677132,
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# Important: test that funl return is in the correct order
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expected_xl=numpy.array([[1.53567906],
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[55.01782167],
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[7.80894889],
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[48.74797493],
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[14.07445705],
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[42.4913859],
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[20.31743841],
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[36.28607535],
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[26.43039605],
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[30.76371366]]),
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expected_funl=numpy.array([-28.44677132, -24.99785984,
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-22.16855376, -18.72136195,
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-15.89423937, -12.45154942,
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-9.63133158, -6.20801301,
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-3.43727232, -0.46353338])
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)
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test2_2 = StructTest2(bounds=[(0, 4.5)],
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expected_x=[1.53567906],
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expected_fun=[-28.44677132],
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expected_xl=numpy.array([[1.53567906]]),
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expected_funl=numpy.array([-28.44677132])
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)
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class StructTest3(StructTestFunction):
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"""
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Hock and Schittkowski 18 problem (HS18). Hoch and Schittkowski (1981)
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http://www.ai7.uni-bayreuth.de/test_problem_coll.pdf
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Minimize: f = 0.01 * (x_1)**2 + (x_2)**2
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Subject to: x_1 * x_2 - 25.0 >= 0,
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(x_1)**2 + (x_2)**2 - 25.0 >= 0,
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2 <= x_1 <= 50,
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0 <= x_2 <= 50.
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Approx. Answer:
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f([(250)**0.5 , (2.5)**0.5]) = 5.0
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"""
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def f(self, x):
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return 0.01 * (x[0]) ** 2 + (x[1]) ** 2
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def g1(x):
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return x[0] * x[1] - 25.0
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def g2(x):
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return x[0] ** 2 + x[1] ** 2 - 25.0
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g = (g1, g2)
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cons = wrap_constraints(g)
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test3_1 = StructTest3(bounds=[(2, 50), (0, 50)],
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expected_x=[250 ** 0.5, 2.5 ** 0.5],
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expected_fun=5.0
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)
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class StructTest4(StructTestFunction):
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"""
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Hock and Schittkowski 11 problem (HS11). Hoch and Schittkowski (1981)
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NOTE: Did not find in original reference to HS collection, refer to
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Henderson (2015) problem 7 instead. 02.03.2016
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"""
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def f(self, x):
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return ((x[0] - 10) ** 2 + 5 * (x[1] - 12) ** 2 + x[2] ** 4
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+ 3 * (x[3] - 11) ** 2 + 10 * x[4] ** 6 + 7 * x[5] ** 2 + x[
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6] ** 4
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- 4 * x[5] * x[6] - 10 * x[5] - 8 * x[6]
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)
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def g1(x):
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return -(2 * x[0] ** 2 + 3 * x[1] ** 4 + x[2] + 4 * x[3] ** 2
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+ 5 * x[4] - 127)
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def g2(x):
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return -(7 * x[0] + 3 * x[1] + 10 * x[2] ** 2 + x[3] - x[4] - 282.0)
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def g3(x):
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return -(23 * x[0] + x[1] ** 2 + 6 * x[5] ** 2 - 8 * x[6] - 196)
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def g4(x):
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return -(4 * x[0] ** 2 + x[1] ** 2 - 3 * x[0] * x[1] + 2 * x[2] ** 2
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+ 5 * x[5] - 11 * x[6])
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g = (g1, g2, g3, g4)
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cons = wrap_constraints(g)
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test4_1 = StructTest4(bounds=[(-10, 10), ] * 7,
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expected_x=[2.330499, 1.951372, -0.4775414,
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4.365726, -0.6244870, 1.038131, 1.594227],
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expected_fun=680.6300573
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)
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class StructTest5(StructTestFunction):
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def f(self, x):
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return (-(x[1] + 47.0)
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* numpy.sin(numpy.sqrt(abs(x[0] / 2.0 + (x[1] + 47.0))))
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- x[0] * numpy.sin(numpy.sqrt(abs(x[0] - (x[1] + 47.0))))
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)
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g = None
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cons = wrap_constraints(g)
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test5_1 = StructTest5(bounds=[(-512, 512), (-512, 512)],
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expected_fun=[-959.64066272085051],
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expected_x=[512., 404.23180542])
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class StructTestLJ(StructTestFunction):
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"""
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LennardJones objective function. Used to test symmetry constraints settings.
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"""
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def f(self, x, *args):
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self.N = args[0]
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k = int(self.N / 3)
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s = 0.0
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for i in range(k - 1):
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for j in range(i + 1, k):
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a = 3 * i
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b = 3 * j
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xd = x[a] - x[b]
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yd = x[a + 1] - x[b + 1]
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zd = x[a + 2] - x[b + 2]
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ed = xd * xd + yd * yd + zd * zd
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ud = ed * ed * ed
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if ed > 0.0:
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s += (1.0 / ud - 2.0) / ud
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return s
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g = None
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cons = wrap_constraints(g)
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N = 6
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boundsLJ = list(zip([-4.0] * 6, [4.0] * 6))
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testLJ = StructTestLJ(bounds=boundsLJ,
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expected_fun=[-1.0],
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expected_x=[-2.71247337e-08,
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-2.71247337e-08,
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-2.50000222e+00,
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-2.71247337e-08,
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-2.71247337e-08,
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-1.50000222e+00]
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)
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class StructTestTable(StructTestFunction):
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def f(self, x):
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if x[0] == 3.0 and x[1] == 3.0:
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return 50
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else:
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return 100
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g = None
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cons = wrap_constraints(g)
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test_table = StructTestTable(bounds=[(-10, 10), (-10, 10)],
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expected_fun=[50],
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expected_x=[3.0, 3.0])
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class StructTestInfeasible(StructTestFunction):
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"""
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Test function with no feasible domain.
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"""
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def f(self, x, *args):
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return x[0] ** 2 + x[1] ** 2
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def g1(x):
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return x[0] + x[1] - 1
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def g2(x):
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return -(x[0] + x[1] - 1)
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def g3(x):
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return -x[0] + x[1] - 1
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def g4(x):
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return -(-x[0] + x[1] - 1)
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g = (g1, g2, g3, g4)
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cons = wrap_constraints(g)
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test_infeasible = StructTestInfeasible(bounds=[(2, 50), (-1, 1)],
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expected_fun=None,
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expected_x=None
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)
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def run_test(test, args=(), test_atol=1e-5, n=100, iters=None,
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callback=None, minimizer_kwargs=None, options=None,
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sampling_method='sobol'):
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res = shgo(test.f, test.bounds, args=args, constraints=test.cons,
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n=n, iters=iters, callback=callback,
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minimizer_kwargs=minimizer_kwargs, options=options,
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sampling_method=sampling_method)
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logging.info(res)
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if test.expected_x is not None:
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numpy.testing.assert_allclose(res.x, test.expected_x,
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rtol=test_atol,
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atol=test_atol)
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# (Optional tests)
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if test.expected_fun is not None:
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numpy.testing.assert_allclose(res.fun,
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test.expected_fun,
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atol=test_atol)
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if test.expected_xl is not None:
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numpy.testing.assert_allclose(res.xl,
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test.expected_xl,
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atol=test_atol)
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if test.expected_funl is not None:
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numpy.testing.assert_allclose(res.funl,
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test.expected_funl,
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atol=test_atol)
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return
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# Base test functions:
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class TestShgoSobolTestFunctions(object):
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"""
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Global optimization tests with Sobol sampling:
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"""
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# Sobol algorithm
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def test_f1_1_sobol(self):
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"""Multivariate test function 1:
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x[0]**2 + x[1]**2 with bounds=[(-1, 6), (-1, 6)]"""
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run_test(test1_1)
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def test_f1_2_sobol(self):
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"""Multivariate test function 1:
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x[0]**2 + x[1]**2 with bounds=[(0, 1), (0, 1)]"""
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run_test(test1_2)
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def test_f1_3_sobol(self):
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"""Multivariate test function 1:
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x[0]**2 + x[1]**2 with bounds=[(None, None),(None, None)]"""
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run_test(test1_3)
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def test_f2_1_sobol(self):
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"""Univariate test function on
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f(x) = (x - 30) * sin(x) with bounds=[(0, 60)]"""
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run_test(test2_1)
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def test_f2_2_sobol(self):
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"""Univariate test function on
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f(x) = (x - 30) * sin(x) bounds=[(0, 4.5)]"""
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run_test(test2_2)
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def test_f3_sobol(self):
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"""NLP: Hock and Schittkowski problem 18"""
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run_test(test3_1)
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@pytest.mark.slow
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def test_f4_sobol(self):
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"""NLP: (High-dimensional) Hock and Schittkowski 11 problem (HS11)"""
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# run_test(test4_1, n=500)
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# run_test(test4_1, n=800)
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options = {'infty_constraints': False}
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run_test(test4_1, n=990, options=options)
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def test_f5_1_sobol(self):
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"""NLP: Eggholder, multimodal"""
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run_test(test5_1, n=30)
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def test_f5_2_sobol(self):
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"""NLP: Eggholder, multimodal"""
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# run_test(test5_1, n=60, iters=5)
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run_test(test5_1, n=60, iters=5)
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# def test_t911(self):
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# """1-D tabletop function"""
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# run_test(test11_1)
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class TestShgoSimplicialTestFunctions(object):
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"""
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Global optimization tests with Simplicial sampling:
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"""
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def test_f1_1_simplicial(self):
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"""Multivariate test function 1:
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x[0]**2 + x[1]**2 with bounds=[(-1, 6), (-1, 6)]"""
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run_test(test1_1, n=1, sampling_method='simplicial')
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def test_f1_2_simplicial(self):
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"""Multivariate test function 1:
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x[0]**2 + x[1]**2 with bounds=[(0, 1), (0, 1)]"""
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run_test(test1_2, n=1, sampling_method='simplicial')
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def test_f1_3_simplicial(self):
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"""Multivariate test function 1: x[0]**2 + x[1]**2
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with bounds=[(None, None),(None, None)]"""
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run_test(test1_3, n=1, sampling_method='simplicial')
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def test_f2_1_simplicial(self):
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"""Univariate test function on
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f(x) = (x - 30) * sin(x) with bounds=[(0, 60)]"""
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options = {'minimize_every_iter': False}
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run_test(test2_1, iters=7, options=options,
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sampling_method='simplicial')
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def test_f2_2_simplicial(self):
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"""Univariate test function on
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f(x) = (x - 30) * sin(x) bounds=[(0, 4.5)]"""
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run_test(test2_2, n=1, sampling_method='simplicial')
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def test_f3_simplicial(self):
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"""NLP: Hock and Schittkowski problem 18"""
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run_test(test3_1, n=1, sampling_method='simplicial')
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@pytest.mark.slow
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def test_f4_simplicial(self):
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"""NLP: (High-dimensional) Hock and Schittkowski 11 problem (HS11)"""
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run_test(test4_1, n=1, sampling_method='simplicial')
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def test_lj_symmetry(self):
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"""LJ: Symmetry-constrained test function"""
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options = {'symmetry': True,
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'disp': True}
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args = (6,) # Number of atoms
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run_test(testLJ, args=args, n=None,
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options=options, iters=4,
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sampling_method='simplicial')
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# Argument test functions
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class TestShgoArguments(object):
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def test_1_1_simpl_iter(self):
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"""Iterative simplicial sampling on TestFunction 1 (multivariate)"""
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run_test(test1_2, n=None, iters=2, sampling_method='simplicial')
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def test_1_2_simpl_iter(self):
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"""Iterative simplicial on TestFunction 2 (univariate)"""
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options = {'minimize_every_iter': False}
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run_test(test2_1, n=None, iters=7, options=options,
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sampling_method='simplicial')
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def test_2_1_sobol_iter(self):
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"""Iterative Sobol sampling on TestFunction 1 (multivariate)"""
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run_test(test1_2, n=None, iters=1, sampling_method='sobol')
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def test_2_2_sobol_iter(self):
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"""Iterative Sobol sampling on TestFunction 2 (univariate)"""
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res = shgo(test2_1.f, test2_1.bounds, constraints=test2_1.cons,
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n=None, iters=1, sampling_method='sobol')
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numpy.testing.assert_allclose(res.x, test2_1.expected_x, rtol=1e-5,
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atol=1e-5)
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numpy.testing.assert_allclose(res.fun, test2_1.expected_fun, atol=1e-5)
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def test_3_1_disp_simplicial(self):
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"""Iterative sampling on TestFunction 1 and 2 (multi- and univariate)"""
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def callback_func(x):
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print("Local minimization callback test")
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for test in [test1_1, test2_1]:
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shgo(test.f, test.bounds, iters=1,
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sampling_method='simplicial',
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callback=callback_func, options={'disp': True})
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shgo(test.f, test.bounds, n=1, sampling_method='simplicial',
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callback=callback_func, options={'disp': True})
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def test_3_2_disp_sobol(self):
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"""Iterative sampling on TestFunction 1 and 2 (multi- and univariate)"""
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def callback_func(x):
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print("Local minimization callback test")
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for test in [test1_1, test2_1]:
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shgo(test.f, test.bounds, iters=1, sampling_method='sobol',
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callback=callback_func, options={'disp': True})
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shgo(test.f, test.bounds, n=1, sampling_method='simplicial',
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callback=callback_func, options={'disp': True})
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@pytest.mark.slow
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def test_4_1_known_f_min(self):
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"""Test known function minima stopping criteria"""
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# Specify known function value
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options = {'f_min': test4_1.expected_fun,
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'f_tol': 1e-6,
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'minimize_every_iter': True}
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# TODO: Make default n higher for faster tests
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run_test(test4_1, n=None, test_atol=1e-5, options=options,
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sampling_method='simplicial')
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@pytest.mark.slow
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def test_4_2_known_f_min(self):
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"""Test Global mode limiting local evalutions"""
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options = { # Specify known function value
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'f_min': test4_1.expected_fun,
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'f_tol': 1e-6,
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# Specify number of local iterations to perform
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'minimize_every_iter': True,
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'local_iter': 1}
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run_test(test4_1, n=None, test_atol=1e-5, options=options,
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sampling_method='simplicial')
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|
|
|
@pytest.mark.slow
|
|
def test_4_3_known_f_min(self):
|
|
"""Test Global mode limiting local evalutions"""
|
|
options = { # Specify known function value
|
|
'f_min': test4_1.expected_fun,
|
|
'f_tol': 1e-6,
|
|
# Specify number of local iterations to perform+
|
|
'minimize_every_iter': True,
|
|
'local_iter': 1,
|
|
'infty_constraints': False}
|
|
|
|
run_test(test4_1, n=300, test_atol=1e-5, options=options,
|
|
sampling_method='sobol')
|
|
|
|
def test_4_4_known_f_min(self):
|
|
"""Test Global mode limiting local evalutions for 1-D functions"""
|
|
options = { # Specify known function value
|
|
'f_min': test2_1.expected_fun,
|
|
'f_tol': 1e-6,
|
|
# Specify number of local iterations to perform+
|
|
'minimize_every_iter': True,
|
|
'local_iter': 1,
|
|
'infty_constraints': False}
|
|
|
|
res = shgo(test2_1.f, test2_1.bounds, constraints=test2_1.cons,
|
|
n=None, iters=None, options=options,
|
|
sampling_method='sobol')
|
|
numpy.testing.assert_allclose(res.x, test2_1.expected_x, rtol=1e-5,
|
|
atol=1e-5)
|
|
|
|
def test_5_1_simplicial_argless(self):
|
|
"""Test Default simplicial sampling settings on TestFunction 1"""
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|
res = shgo(test1_1.f, test1_1.bounds, constraints=test1_1.cons)
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|
numpy.testing.assert_allclose(res.x, test1_1.expected_x, rtol=1e-5,
|
|
atol=1e-5)
|
|
|
|
def test_5_2_sobol_argless(self):
|
|
"""Test Default sobol sampling settings on TestFunction 1"""
|
|
res = shgo(test1_1.f, test1_1.bounds, constraints=test1_1.cons,
|
|
sampling_method='sobol')
|
|
numpy.testing.assert_allclose(res.x, test1_1.expected_x, rtol=1e-5,
|
|
atol=1e-5)
|
|
|
|
def test_6_1_simplicial_max_iter(self):
|
|
"""Test that maximum iteration option works on TestFunction 3"""
|
|
options = {'max_iter': 2}
|
|
res = shgo(test3_1.f, test3_1.bounds, constraints=test3_1.cons,
|
|
options=options, sampling_method='simplicial')
|
|
numpy.testing.assert_allclose(res.x, test3_1.expected_x, rtol=1e-5,
|
|
atol=1e-5)
|
|
numpy.testing.assert_allclose(res.fun, test3_1.expected_fun, atol=1e-5)
|
|
|
|
def test_6_2_simplicial_min_iter(self):
|
|
"""Test that maximum iteration option works on TestFunction 3"""
|
|
options = {'min_iter': 2}
|
|
res = shgo(test3_1.f, test3_1.bounds, constraints=test3_1.cons,
|
|
options=options, sampling_method='simplicial')
|
|
numpy.testing.assert_allclose(res.x, test3_1.expected_x, rtol=1e-5,
|
|
atol=1e-5)
|
|
numpy.testing.assert_allclose(res.fun, test3_1.expected_fun, atol=1e-5)
|
|
|
|
def test_7_1_minkwargs(self):
|
|
"""Test the minimizer_kwargs arguments for solvers with constraints"""
|
|
# Test solvers
|
|
for solver in ['COBYLA', 'SLSQP']:
|
|
# Note that passing global constraints to SLSQP is tested in other
|
|
# unittests which run test4_1 normally
|
|
minimizer_kwargs = {'method': solver,
|
|
'constraints': test3_1.cons}
|
|
print("Solver = {}".format(solver))
|
|
print("=" * 100)
|
|
run_test(test3_1, n=100, test_atol=1e-3,
|
|
minimizer_kwargs=minimizer_kwargs, sampling_method='sobol')
|
|
|
|
def test_7_2_minkwargs(self):
|
|
"""Test the minimizer_kwargs default inits"""
|
|
minimizer_kwargs = {'ftol': 1e-5}
|
|
options = {'disp': True} # For coverage purposes
|
|
SHGO(test3_1.f, test3_1.bounds, constraints=test3_1.cons[0],
|
|
minimizer_kwargs=minimizer_kwargs, options=options)
|
|
|
|
def test_7_3_minkwargs(self):
|
|
"""Test minimizer_kwargs arguments for solvers without constraints"""
|
|
for solver in ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG',
|
|
'L-BFGS-B', 'TNC', 'dogleg', 'trust-ncg', 'trust-exact',
|
|
'trust-krylov']:
|
|
def jac(x):
|
|
return numpy.array([2 * x[0], 2 * x[1]]).T
|
|
|
|
def hess(x):
|
|
return numpy.array([[2, 0], [0, 2]])
|
|
|
|
minimizer_kwargs = {'method': solver,
|
|
'jac': jac,
|
|
'hess': hess}
|
|
logging.info("Solver = {}".format(solver))
|
|
logging.info("=" * 100)
|
|
run_test(test1_1, n=100, test_atol=1e-3,
|
|
minimizer_kwargs=minimizer_kwargs, sampling_method='sobol')
|
|
|
|
def test_8_homology_group_diff(self):
|
|
options = {'minhgrd': 1,
|
|
'minimize_every_iter': True}
|
|
|
|
run_test(test1_1, n=None, iters=None, options=options,
|
|
sampling_method='simplicial')
|
|
|
|
def test_9_cons_g(self):
|
|
"""Test single function constraint passing"""
|
|
SHGO(test3_1.f, test3_1.bounds, constraints=test3_1.cons[0])
|
|
|
|
def test_10_finite_time(self):
|
|
"""Test single function constraint passing"""
|
|
options = {'maxtime': 1e-15}
|
|
shgo(test1_1.f, test1_1.bounds, n=1, iters=None,
|
|
options=options, sampling_method='sobol')
|
|
|
|
def test_11_f_min_time(self):
|
|
"""Test to cover the case where f_lowest == 0"""
|
|
options = {'maxtime': 1e-15,
|
|
'f_min': 0.0}
|
|
shgo(test1_2.f, test1_2.bounds, n=1, iters=None,
|
|
options=options, sampling_method='sobol')
|
|
|
|
def test_12_sobol_inf_cons(self):
|
|
"""Test to cover the case where f_lowest == 0"""
|
|
options = {'maxtime': 1e-15,
|
|
'f_min': 0.0}
|
|
shgo(test1_2.f, test1_2.bounds, n=1, iters=None,
|
|
options=options, sampling_method='sobol')
|
|
|
|
def test_13_high_sobol(self):
|
|
"""Test init of high-dimensional sobol sequences"""
|
|
|
|
def f(x):
|
|
return 0
|
|
|
|
bounds = [(None, None), ] * 41
|
|
SHGOc = SHGO(f, bounds)
|
|
SHGOc.sobol_points(2, 50)
|
|
|
|
def test_14_local_iter(self):
|
|
"""Test limited local iterations for a pseudo-global mode"""
|
|
options = {'local_iter': 4}
|
|
run_test(test5_1, n=30, options=options)
|
|
|
|
def test_15_min_every_iter(self):
|
|
"""Test minimize every iter options and cover function cache"""
|
|
options = {'minimize_every_iter': True}
|
|
run_test(test1_1, n=1, iters=7, options=options,
|
|
sampling_method='sobol')
|
|
|
|
def test_16_disp_bounds_minimizer(self):
|
|
"""Test disp=True with minimizers that do not support bounds """
|
|
options = {'disp': True}
|
|
minimizer_kwargs = {'method': 'nelder-mead'}
|
|
run_test(test1_2, sampling_method='simplicial',
|
|
options=options, minimizer_kwargs=minimizer_kwargs)
|
|
|
|
def test_17_custom_sampling(self):
|
|
"""Test the functionality to add custom sampling methods to shgo"""
|
|
def sample(n, d):
|
|
return numpy.random.uniform(size=(n,d))
|
|
|
|
run_test(test1_1, n=30, sampling_method=sample)
|
|
|
|
# Failure test functions
|
|
class TestShgoFailures(object):
|
|
def test_1_maxiter(self):
|
|
"""Test failure on insufficient iterations"""
|
|
options = {'maxiter': 2}
|
|
res = shgo(test4_1.f, test4_1.bounds, n=2, iters=None,
|
|
options=options, sampling_method='sobol')
|
|
|
|
numpy.testing.assert_equal(False, res.success)
|
|
numpy.testing.assert_equal(4, res.nfev)
|
|
|
|
def test_2_sampling(self):
|
|
"""Rejection of unknown sampling method"""
|
|
assert_raises(ValueError, shgo, test1_1.f, test1_1.bounds,
|
|
sampling_method='not_Sobol')
|
|
|
|
def test_3_1_no_min_pool_sobol(self):
|
|
"""Check that the routine stops when no minimiser is found
|
|
after maximum specified function evaluations"""
|
|
options = {'maxfev': 10,
|
|
'disp': True}
|
|
res = shgo(test_table.f, test_table.bounds, n=3, options=options,
|
|
sampling_method='sobol')
|
|
numpy.testing.assert_equal(False, res.success)
|
|
# numpy.testing.assert_equal(9, res.nfev)
|
|
numpy.testing.assert_equal(12, res.nfev)
|
|
|
|
def test_3_2_no_min_pool_simplicial(self):
|
|
"""Check that the routine stops when no minimiser is found
|
|
after maximum specified sampling evaluations"""
|
|
options = {'maxev': 10,
|
|
'disp': True}
|
|
res = shgo(test_table.f, test_table.bounds, n=3, options=options,
|
|
sampling_method='simplicial')
|
|
numpy.testing.assert_equal(False, res.success)
|
|
|
|
def test_4_1_bound_err(self):
|
|
"""Specified bounds ub > lb"""
|
|
bounds = [(6, 3), (3, 5)]
|
|
assert_raises(ValueError, shgo, test1_1.f, bounds)
|
|
|
|
def test_4_2_bound_err(self):
|
|
"""Specified bounds are of the form (lb, ub)"""
|
|
bounds = [(3, 5, 5), (3, 5)]
|
|
assert_raises(ValueError, shgo, test1_1.f, bounds)
|
|
|
|
def test_5_1_1_infeasible_sobol(self):
|
|
"""Ensures the algorithm terminates on infeasible problems
|
|
after maxev is exceeded. Use infty constraints option"""
|
|
options = {'maxev': 100,
|
|
'disp': True}
|
|
|
|
res = shgo(test_infeasible.f, test_infeasible.bounds,
|
|
constraints=test_infeasible.cons, n=100, options=options,
|
|
sampling_method='sobol')
|
|
|
|
numpy.testing.assert_equal(False, res.success)
|
|
|
|
def test_5_1_2_infeasible_sobol(self):
|
|
"""Ensures the algorithm terminates on infeasible problems
|
|
after maxev is exceeded. Do not use infty constraints option"""
|
|
options = {'maxev': 100,
|
|
'disp': True,
|
|
'infty_constraints': False}
|
|
|
|
res = shgo(test_infeasible.f, test_infeasible.bounds,
|
|
constraints=test_infeasible.cons, n=100, options=options,
|
|
sampling_method='sobol')
|
|
|
|
numpy.testing.assert_equal(False, res.success)
|
|
|
|
def test_5_2_infeasible_simplicial(self):
|
|
"""Ensures the algorithm terminates on infeasible problems
|
|
after maxev is exceeded."""
|
|
options = {'maxev': 1000,
|
|
'disp': False}
|
|
|
|
res = shgo(test_infeasible.f, test_infeasible.bounds,
|
|
constraints=test_infeasible.cons, n=100, options=options,
|
|
sampling_method='simplicial')
|
|
|
|
numpy.testing.assert_equal(False, res.success)
|
|
|
|
def test_6_1_lower_known_f_min(self):
|
|
"""Test Global mode limiting local evalutions with f* too high"""
|
|
options = { # Specify known function value
|
|
'f_min': test2_1.expected_fun + 2.0,
|
|
'f_tol': 1e-6,
|
|
# Specify number of local iterations to perform+
|
|
'minimize_every_iter': True,
|
|
'local_iter': 1,
|
|
'infty_constraints': False}
|
|
args = (test2_1.f, test2_1.bounds)
|
|
kwargs = {'constraints': test2_1.cons,
|
|
'n': None,
|
|
'iters': None,
|
|
'options': options,
|
|
'sampling_method': 'sobol'
|
|
}
|
|
warns(UserWarning, shgo, *args, **kwargs)
|