test_minimizers.py 8.28 KB
Newer Older
Philipp Arras's avatar
Philipp Arras committed
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
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2019 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.

from unittest import SkipTest

import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_equal

import nifty5 as ift

pmp = pytest.mark.parametrize
IC = ift.GradientNormController(tol_abs_gradnorm=1e-5, iteration_limit=1000)

spaces = [ift.RGSpace([1024], distances=0.123), ift.HPSpace(32)]

minimizers = [
    'ift.VL_BFGS(IC)',
    'ift.NonlinearCG(IC, "Polak-Ribiere")',
    # 'ift.NonlinearCG(IC, "Hestenes-Stiefel"),
    'ift.NonlinearCG(IC, "Fletcher-Reeves")',
    'ift.NonlinearCG(IC, "5.49")',
    'ift.L_BFGS_B(ftol=1e-10, gtol=1e-5, maxiter=1000)',
    'ift.L_BFGS(IC)',
    'ift.NewtonCG(IC)'
]

newton_minimizers = ['ift.RelaxedNewton(IC)']
quadratic_only_minimizers = [
Martin Reinecke's avatar
Martin Reinecke committed
44 45
    'ift.ConjugateGradient(IC)',
    'ift.minimization.scipy_minimizer._ScipyCG(tol=1e-5, maxiter=300)'
Philipp Arras's avatar
Philipp Arras committed
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
]
slow_minimizers = ['ift.SteepestDescent(IC)']


@pmp('minimizer', minimizers + newton_minimizers + quadratic_only_minimizers +
     slow_minimizers)
@pmp('space', spaces)
def test_quadratic_minimization(minimizer, space):
    np.random.seed(42)
    starting_point = ift.Field.from_random('normal', domain=space)*10
    covariance_diagonal = ift.Field.from_random('uniform', domain=space) + 0.5
    covariance = ift.DiagonalOperator(covariance_diagonal)
    required_result = ift.full(space, 1.)

    try:
        minimizer = eval(minimizer)
        energy = ift.QuadraticEnergy(
            A=covariance, b=required_result, position=starting_point)

        (energy, convergence) = minimizer(energy)
    except NotImplementedError:
        raise SkipTest

    assert_equal(convergence, IC.CONVERGED)
    assert_allclose(
        energy.position.local_data,
        1./covariance_diagonal.local_data,
        rtol=1e-3,
        atol=1e-3)

Martin Reinecke's avatar
Martin Reinecke committed
76

77 78 79 80 81 82 83 84 85 86 87 88
@pmp('space', spaces)
def test_WF_curvature(space):
    np.random.seed(42)
    required_result = ift.full(space, 1.)

    s = ift.Field.from_random('uniform', domain=space) + 0.5
    S = ift.DiagonalOperator(s)
    r = ift.Field.from_random('uniform', domain=space)
    R = ift.DiagonalOperator(r)
    n = ift.Field.from_random('uniform', domain=space) + 0.5
    N = ift.DiagonalOperator(n)
    all_diag = 1./s + r**2/n
Martin Reinecke's avatar
Martin Reinecke committed
89 90
    curv = ift.WienerFilterCurvature(R, N, S, iteration_controller=IC,
                                     iteration_controller_sampling=IC)
91 92 93 94 95 96 97 98 99
    m = curv.inverse(required_result)
    assert_allclose(
        m.local_data,
        1./all_diag.local_data,
        rtol=1e-3,
        atol=1e-3)
    curv.draw_sample()
    curv.draw_sample(from_inverse=True)

100 101 102 103 104
    if len(space.shape) == 1:
        R = ift.ValueInserter(space, [0])
        n = ift.from_random('uniform', R.domain) + 0.5
        N = ift.DiagonalOperator(n)
        all_diag = 1./s + R(1/n)
Martin Reinecke's avatar
Martin Reinecke committed
105 106 107
        curv = ift.WienerFilterCurvature(R.adjoint, N, S,
                                         iteration_controller=IC,
                                         iteration_controller_sampling=IC)
108 109 110 111 112 113 114 115 116 117
        m = curv.inverse(required_result)
        assert_allclose(
            m.local_data,
            1./all_diag.local_data,
            rtol=1e-3,
            atol=1e-3)
        curv.draw_sample()
        curv.draw_sample(from_inverse=True)


Philipp Arras's avatar
Philipp Arras committed
118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
@pmp('minimizer', minimizers + newton_minimizers)
def test_rosenbrock(minimizer):
    try:
        from scipy.optimize import rosen, rosen_der, rosen_hess_prod
    except ImportError:
        raise SkipTest
    np.random.seed(42)
    space = ift.DomainTuple.make(ift.UnstructuredDomain((2,)))
    starting_point = ift.Field.from_random('normal', domain=space)*10

    class RBEnergy(ift.Energy):
        def __init__(self, position):
            super(RBEnergy, self).__init__(position)

        @property
        def value(self):
            return rosen(self._position.to_global_data_rw())

        @property
        def gradient(self):
            inp = self._position.to_global_data_rw()
            out = ift.Field.from_global_data(space, rosen_der(inp))
            return out

        @property
        def metric(self):
            class RBCurv(ift.EndomorphicOperator):
                def __init__(self, loc):
                    self._loc = loc.to_global_data_rw()
                    self._capability = self.TIMES
                    self._domain = space

                def apply(self, x, mode):
                    self._check_input(x, mode)
                    inp = x.to_global_data_rw()
                    out = ift.Field.from_global_data(
                        space, rosen_hess_prod(self._loc.copy(), inp))
                    return out

            t1 = ift.GradientNormController(
                tol_abs_gradnorm=1e-5, iteration_limit=1000)
            return ift.InversionEnabler(RBCurv(self._position), t1)

        def apply_metric(self, x):
            inp = x.to_global_data_rw()
            pos = self._position.to_global_data_rw()
            return ift.Field.from_global_data(space, rosen_hess_prod(pos, inp))

    try:
        minimizer = eval(minimizer)
        energy = RBEnergy(position=starting_point)

        (energy, convergence) = minimizer(energy)
    except NotImplementedError:
        raise SkipTest

    assert_equal(convergence, IC.CONVERGED)
    assert_allclose(energy.position.local_data, 1., rtol=1e-3, atol=1e-3)


@pmp('minimizer', minimizers + slow_minimizers)
def test_gauss(minimizer):
    space = ift.UnstructuredDomain((1,))
    starting_point = ift.Field.full(space, 3.)

    class ExpEnergy(ift.Energy):
        def __init__(self, position):
            super(ExpEnergy, self).__init__(position)

        @property
        def value(self):
            x = self.position.to_global_data()[0]
            return -np.exp(-(x**2))

        @property
        def gradient(self):
            x = self.position.to_global_data()[0]
            return ift.Field.full(self.position.domain, 2*x*np.exp(-(x**2)))

        def apply_metric(self, x):
            p = self.position.to_global_data()[0]
            v = (2 - 4*p*p)*np.exp(-p**2)
            return ift.DiagonalOperator(
                ift.Field.full(self.position.domain, v))(x)

    try:
        minimizer = eval(minimizer)
        energy = ExpEnergy(position=starting_point)

        (energy, convergence) = minimizer(energy)
    except NotImplementedError:
        raise SkipTest

    assert_equal(convergence, IC.CONVERGED)
    assert_allclose(energy.position.local_data, 0., atol=1e-3)


@pmp('minimizer', minimizers + newton_minimizers + slow_minimizers)
def test_cosh(minimizer):
    space = ift.UnstructuredDomain((1,))
    starting_point = ift.Field.full(space, 3.)

    class CoshEnergy(ift.Energy):
        def __init__(self, position):
            super(CoshEnergy, self).__init__(position)

        @property
        def value(self):
            x = self.position.to_global_data()[0]
            return np.cosh(x)

        @property
        def gradient(self):
            x = self.position.to_global_data()[0]
            return ift.Field.full(self.position.domain, np.sinh(x))

        @property
        def metric(self):
            x = self.position.to_global_data()[0]
            v = np.cosh(x)
            return ift.DiagonalOperator(
                ift.Field.full(self.position.domain, v))

        def apply_metric(self, x):
            p = self.position.to_global_data()[0]
            v = np.cosh(p)
            return ift.DiagonalOperator(
                ift.Field.full(self.position.domain, v))(x)

    try:
        minimizer = eval(minimizer)
        energy = CoshEnergy(position=starting_point)

        (energy, convergence) = minimizer(energy)
    except NotImplementedError:
        raise SkipTest

    assert_equal(convergence, IC.CONVERGED)
    assert_allclose(energy.position.local_data, 0., atol=1e-3)