Python _differentialevolution.DifferentialEvolutionSolver类代码示例

 def test_deferred_updating(self):
     # check setting of deferred updating, with default workers
     bounds = [(0., 2.), (0., 2.)]
     solver = DifferentialEvolutionSolver(rosen, bounds, updating='deferred')
     assert_(solver._updating == 'deferred')
     assert_(solver._mapwrapper._mapfunc is map)
     solver.solve()

 def test_quadratic(self):
     # test the quadratic function from object
     solver = DifferentialEvolutionSolver(self.quadratic,
                                          [(-100, 100)],
                                          tol=0.02)
     solver.solve()
     assert_equal(np.argmin(solver.population_energies), 0)

    def test_maxfun_stops_solve(self):
        # test that if the maximum number of function evaluations is exceeded
        # during initialisation the solver stops
        solver = DifferentialEvolutionSolver(rosen, self.bounds, maxfun=1,
                                             polish=False)
        result = solver.solve()

        assert_equal(result.nfev, 2)
        assert_equal(result.success, False)
        assert_equal(result.message,
                     'Maximum number of function evaluations has '
                     'been exceeded.')

        # test that if the maximum number of function evaluations is exceeded
        # during the actual minimisation, then the solver stops.
        # Have to turn polishing off, as this will still occur even if maxfun
        # is reached. For popsize=5 and len(bounds)=2, then there are only 10
        # function evaluations during initialisation.
        solver = DifferentialEvolutionSolver(rosen,
                                             self.bounds,
                                             popsize=5,
                                             polish=False,
                                             maxfun=40)
        result = solver.solve()

        assert_equal(result.nfev, 41)
        assert_equal(result.success, False)
        assert_equal(result.message,
                         'Maximum number of function evaluations has '
                              'been exceeded.')

 def test_maxiter_stops_solve(self):
     # test that if the maximum number of iterations is exceeded
     # the solver stops.
     solver = DifferentialEvolutionSolver(rosen, self.bounds, maxiter=1)
     result = solver.solve()
     assert_equal(result.success, False)
     assert_equal(result.message,
                     'Maximum number of iterations has been exceeded.')

    def test_exp_runs(self):
        # test whether exponential mutation loop runs
        solver = DifferentialEvolutionSolver(rosen,
                                             self.bounds,
                                             strategy='best1exp',
                                             maxiter=1)

        solver.solve()

 def test_maxiter_none_GH5731(self):
     # Pre 0.17 the previous default for maxiter and maxfun was None.
     # the numerical defaults are now 1000 and np.inf. However, some scripts
     # will still supply None for both of those, this will raise a TypeError
     # in the solve method.
     solver = DifferentialEvolutionSolver(rosen, self.bounds, maxiter=None,
                                          maxfun=None)
     solver.solve()

 def test_select_samples(self):
     # select_samples should return 5 separate random numbers.
     limits = np.arange(12., dtype='float64').reshape(2, 6)
     bounds = list(zip(limits[0, :], limits[1, :]))
     solver = DifferentialEvolutionSolver(None, bounds, popsize=1)
     candidate = 0
     r1, r2, r3, r4, r5 = solver._select_samples(candidate, 5)
     assert_equal(
         len(np.unique(np.array([candidate, r1, r2, r3, r4, r5]))), 6)

    def test_calculate_population_energies(self):
        # if popsize is 3 then the overall generation has size (6,)
        solver = DifferentialEvolutionSolver(rosen, self.bounds, popsize=3)
        solver._calculate_population_energies(solver.population)
        solver._promote_lowest_energy()
        assert_equal(np.argmin(solver.population_energies), 0)

        # initial calculation of the energies should require 6 nfev.
        assert_equal(solver._nfev, 6)

    def test_constraint_solve(self):
        def constr_f(x):
            return np.array([x[0] + x[1]])

        nlc = NonlinearConstraint(constr_f, -np.inf, 1.9)

        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
                                             constraints=(nlc))

        # trust-constr warns if the constraint function is linear
        with warns(UserWarning):
            res = solver.solve()

        assert constr_f(res.x) <= 1.9
        assert res.success

    def setup_method(self):
        self.old_seterr = np.seterr(invalid='raise')
        self.limits = np.array([[0., 0.],
                                [2., 2.]])
        self.bounds = [(0., 2.), (0., 2.)]

        self.dummy_solver = DifferentialEvolutionSolver(self.quadratic,
                                                        [(0, 100)])

        # dummy_solver2 will be used to test mutation strategies
        self.dummy_solver2 = DifferentialEvolutionSolver(self.quadratic,
                                                         [(0, 1)],
                                                         popsize=7,
                                                         mutation=0.5)
        # create a population that's only 7 members long
        # [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
        population = np.atleast_2d(np.arange(0.1, 0.8, 0.1)).T
        self.dummy_solver2.population = population

    def test_constraint_violation_fn(self):
        def constr_f(x):
            return [x[0] + x[1]]

        def constr_f2(x):
            return [x[0]**2 + x[1], x[0] - x[1]]

        nlc = NonlinearConstraint(constr_f, -np.inf, 1.9)

        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
                                             constraints=(nlc))

        cv = solver._constraint_violation_fn([1.0, 1.0])
        assert_almost_equal(cv, 0.1)

        nlc2 = NonlinearConstraint(constr_f2, -np.inf, 1.8)
        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
                                             constraints=(nlc, nlc2))

        # for multiple constraints the constraint violations should
        # be concatenated.
        cv = solver._constraint_violation_fn([1.2, 1.])
        assert_almost_equal(cv, [0.3, 0.64, 0])

        cv = solver._constraint_violation_fn([2., 2.])
        assert_almost_equal(cv, [2.1, 4.2, 0])

        # should accept valid values
        cv = solver._constraint_violation_fn([0.5, 0.5])
        assert_almost_equal(cv, [0., 0., 0.])

    def test_population_initiation(self):
        # test the different modes of population initiation

        # init must be either 'latinhypercube' or 'random'
        # raising ValueError is something else is passed in
        assert_raises(ValueError,
                      DifferentialEvolutionSolver,
                      *(rosen, self.bounds),
                      **{'init': 'rubbish'})

        solver = DifferentialEvolutionSolver(rosen, self.bounds)

        # check that population initiation:
        # 1) resets _nfev to 0
        # 2) all population energies are np.inf
        solver.init_population_random()
        assert_equal(solver._nfev, 0)
        assert_(np.all(np.isinf(solver.population_energies)))

        solver.init_population_lhs()
        assert_equal(solver._nfev, 0)
        assert_(np.all(np.isinf(solver.population_energies)))

    def test_population_initiation(self):
        # test the different modes of population initiation

        # init must be either 'latinhypercube' or 'random'
        # raising ValueError is something else is passed in
        assert_raises(ValueError,
                      DifferentialEvolutionSolver,
                      *(rosen, self.bounds),
                      **{'init': 'rubbish'})

        solver = DifferentialEvolutionSolver(rosen, self.bounds)

        # check that population initiation:
        # 1) resets _nfev to 0
        # 2) all population energies are np.inf
        solver.init_population_random()
        assert_equal(solver._nfev, 0)
        assert_(np.all(np.isinf(solver.population_energies)))

        solver.init_population_lhs()
        assert_equal(solver._nfev, 0)
        assert_(np.all(np.isinf(solver.population_energies)))

        # we should be able to initialise with our own array
        population = np.linspace(-1, 3, 10).reshape(5, 2)
        solver = DifferentialEvolutionSolver(rosen, self.bounds,
                                             init=population,
                                             strategy='best2bin',
                                             atol=0.01, seed=1, popsize=5)

        assert_equal(solver._nfev, 0)
        assert_(np.all(np.isinf(solver.population_energies)))
        assert_(solver.num_population_members == 5)
        assert_(solver.population_shape == (5, 2))

        # check that the population was initialised correctly
        unscaled_population = np.clip(solver._unscale_parameters(population),
                                      0, 1)
        assert_almost_equal(solver.population[:5], unscaled_population)

        # population values need to be clipped to bounds
        assert_almost_equal(np.min(solver.population[:5]), 0)
        assert_almost_equal(np.max(solver.population[:5]), 1)

        # shouldn't be able to initialise with an array if it's the wrong shape
        # this would have too many parameters
        population = np.linspace(-1, 3, 15).reshape(5, 3)
        assert_raises(ValueError,
                      DifferentialEvolutionSolver,
                      *(rosen, self.bounds),
                      **{'init': population})

    def test_impossible_constraint(self):
        def constr_f(x):
            return np.array([x[0] + x[1]])

        nlc = NonlinearConstraint(constr_f, -np.inf, -1)

        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
                                             constraints=(nlc), popsize=3)

        # a UserWarning is issued because the 'trust-constr' polishing is
        # attempted on the least infeasible solution found.
        with warns(UserWarning):
            res = solver.solve()

        assert_allclose(res.x, [0, 0], atol=1e-6)
        assert res.maxcv > 0
        assert not res.success

        # test _promote_lowest_energy works when none of the population is
        # feasible. In this case the solution with the lowest constraint
        # violation should be promoted.
        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
                                             constraints=(nlc), polish=False)
        next(solver)
        assert not solver.feasible.all()
        assert not np.isfinite(solver.population_energies).all()

        # now swap two of the entries in the population
        l = 20
        cv = solver.constraint_violation[0]

        solver.population_energies[[0, l]] = solver.population_energies[[l, 0]]
        solver.population[[0, l], :] = solver.population[[l, 0], :]
        solver.constraint_violation[[0, l], :] = (
            solver.constraint_violation[[l, 0], :])

        solver._promote_lowest_energy()
        assert_equal(solver.constraint_violation[0], cv)

    def test_constraint_population_feasibilities(self):
        def constr_f(x):
            return [x[0] + x[1]]

        def constr_f2(x):
            return [x[0]**2 + x[1], x[0] - x[1]]

        nlc = NonlinearConstraint(constr_f, -np.inf, 1.9)

        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
                                             constraints=(nlc))

        # are population feasibilities correct
        # [0.5, 0.5] corresponds to scaled values of [1., 1.]
        feas, cv = solver._calculate_population_feasibilities(
            np.array([[0.5, 0.5], [1., 1.]]))
        assert_equal(feas, [False, False])
        assert_almost_equal(cv, np.array([[0.1], [2.1]]))
        assert cv.shape == (2, 1)

        nlc2 = NonlinearConstraint(constr_f2, -np.inf, 1.8)
        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
                                             constraints=(nlc, nlc2))

        feas, cv = solver._calculate_population_feasibilities(
            np.array([[0.5, 0.5], [0.6, 0.5]]))
        assert_equal(feas, [False, False])
        assert_almost_equal(cv, np.array([[0.1, 0.2, 0], [0.3, 0.64, 0]]))

        feas, cv = solver._calculate_population_feasibilities(
            np.array([[0.5, 0.5], [1., 1.]]))
        assert_equal(feas, [False, False])
        assert_almost_equal(cv, np.array([[0.1, 0.2, 0], [2.1, 4.2, 0]]))
        assert cv.shape == (2, 3)

        feas, cv = solver._calculate_population_feasibilities(
            np.array([[0.25, 0.25], [1., 1.]]))
        assert_equal(feas, [True, False])
        assert_almost_equal(cv, np.array([[0.0, 0.0, 0.], [2.1, 4.2, 0]]))
        assert cv.shape == (2, 3)