test_jacobian.py 6.6 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
# 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.

import numpy as np
import pytest

Martin Reinecke's avatar
5->6    
Martin Reinecke committed
21
import nifty6 as ift
Philipp Arras's avatar
Philipp Arras committed
22

23
from ..common import list2fixture, setup_function, teardown_function
Philipp Arras's avatar
Philipp Arras committed
24
25
26
27
28
29
30

pmp = pytest.mark.parametrize
space = list2fixture([
    ift.GLSpace(15),
    ift.RGSpace(64, distances=.789),
    ift.RGSpace([32, 32], distances=.789)
])
Philipp Arras's avatar
Philipp Arras committed
31
32
33
34
35
_h_RG_spaces = [
    ift.RGSpace(7, distances=0.2, harmonic=True),
    ift.RGSpace((12, 46), distances=(.2, .3), harmonic=True)
]
_h_spaces = _h_RG_spaces + [ift.LMSpace(17)]
Philipp Arras's avatar
Philipp Arras committed
36
37
38
39
space1 = space
seed = list2fixture([4, 78, 23])


Philipp Arras's avatar
Philipp Arras committed
40
def testBasics(space, seed):
Martin Reinecke's avatar
Martin Reinecke committed
41
    with ift.random.Context(seed):
Philipp Arras's avatar
Philipp Arras committed
42
        s = ift.from_random('normal', space)
Martin Reinecke's avatar
Martin Reinecke committed
43
44
45
        var = ift.Linearization.make_var(s)
        model = ift.ScalingOperator(var.target, 6.)
        ift.extra.check_jacobian_consistency(model, var.val)
Philipp Arras's avatar
Philipp Arras committed
46
47
48
49
50


@pmp('type1', ['Variable', 'Constant'])
@pmp('type2', ['Variable'])
def testBinary(type1, type2, space, seed):
Martin Reinecke's avatar
Martin Reinecke committed
51
52
53
54
55
56
57
    with ift.random.Context(seed):
        dom1 = ift.MultiDomain.make({'s1': space})
        dom2 = ift.MultiDomain.make({'s2': space})
        dom = ift.MultiDomain.union((dom1, dom2))
        select_s1 = ift.ducktape(None, dom1, "s1")
        select_s2 = ift.ducktape(None, dom2, "s2")
        model = select_s1*select_s2
Philipp Arras's avatar
Philipp Arras committed
58
        pos = ift.from_random("normal", dom)
Martin Reinecke's avatar
Martin Reinecke committed
59
        ift.extra.check_jacobian_consistency(model, pos, ntries=20)
Martin Reinecke's avatar
Martin Reinecke committed
60
61
62
63
64
65
66
67
68
        model = select_s1 + select_s2
        pos = ift.from_random("normal", dom)
        ift.extra.check_jacobian_consistency(model, pos, ntries=20)
        model = select_s1.scale(3.)
        pos = ift.from_random("normal", dom1)
        ift.extra.check_jacobian_consistency(model, pos, ntries=20)
        model = ift.ScalingOperator(space, 2.456)(select_s1*select_s2)
        pos = ift.from_random("normal", dom)
        ift.extra.check_jacobian_consistency(model, pos, ntries=20)
Martin Reinecke's avatar
Martin Reinecke committed
69
        model = (2.456*(select_s1*select_s2)).ptw("sigmoid")
Martin Reinecke's avatar
Martin Reinecke committed
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
        pos = ift.from_random("normal", dom)
        ift.extra.check_jacobian_consistency(model, pos, ntries=20)
        pos = ift.from_random("normal", dom)
        model = ift.OuterProduct(pos['s1'], ift.makeDomain(space))
        ift.extra.check_jacobian_consistency(model, pos['s2'], ntries=20)
        model = select_s1**2
        pos = ift.from_random("normal", dom1)
        ift.extra.check_jacobian_consistency(model, pos, ntries=20)
        model = select_s1.clip(-1, 1)
        pos = ift.from_random("normal", dom1)
        ift.extra.check_jacobian_consistency(model, pos, ntries=20)
        f = ift.from_random("normal", space)
        model = select_s1.clip(f-0.1, f+1.)
        pos = ift.from_random("normal", dom1)
        ift.extra.check_jacobian_consistency(model, pos, ntries=20)
        if isinstance(space, ift.RGSpace):
            model = ift.FFTOperator(space)(select_s1*select_s2)
            pos = ift.from_random("normal", dom)
            ift.extra.check_jacobian_consistency(model, pos, ntries=20)
Philipp Arras's avatar
Philipp Arras committed
89
90


Rouven Lemmerz's avatar
Rouven Lemmerz committed
91
def testSpecialDistributionOps(space, seed):
Martin Reinecke's avatar
Martin Reinecke committed
92
    with ift.random.Context(seed):
Philipp Arras's avatar
Philipp Arras committed
93
        pos = ift.from_random('normal', space)
Martin Reinecke's avatar
Martin Reinecke committed
94
95
96
97
98
99
        alpha = 1.5
        q = 0.73
        model = ift.InverseGammaOperator(space, alpha, q)
        ift.extra.check_jacobian_consistency(model, pos, ntries=20)
        model = ift.UniformOperator(space, alpha, q)
        ift.extra.check_jacobian_consistency(model, pos, ntries=20)
Martin Reinecke's avatar
Martin Reinecke committed
100

101

Philipp Arras's avatar
Philipp Arras committed
102
103
@pmp('neg', [True, False])
def testAdder(space, seed, neg):
Martin Reinecke's avatar
Martin Reinecke committed
104
    with ift.random.Context(seed):
Philipp Arras's avatar
Philipp Arras committed
105
106
        f = ift.from_random('normal', space)
        f1 = ift.from_random('normal', space)
Martin Reinecke's avatar
Martin Reinecke committed
107
108
109
110
        op = ift.Adder(f1, neg)
        ift.extra.check_jacobian_consistency(op, f)
        op = ift.Adder(f1.val.ravel()[0], neg=neg, domain=space)
        ift.extra.check_jacobian_consistency(op, f)
Philipp Arras's avatar
Philipp Arras committed
111
112
113
114
115


@pmp('target', [ift.RGSpace(64, distances=.789, harmonic=True),
                ift.RGSpace([32, 32], distances=.789, harmonic=True),
                ift.RGSpace([32, 32, 8], distances=.789, harmonic=True)])
Martin Reinecke's avatar
Martin Reinecke committed
116
117
@pmp('causal', [True, False])
@pmp('minimum_phase', [True, False])
Philipp Frank's avatar
Philipp Frank committed
118
def testDynamicModel(target, causal, minimum_phase, seed):
Martin Reinecke's avatar
Martin Reinecke committed
119
    with ift.random.Context(seed):
120
        dct = {
Martin Reinecke's avatar
Martin Reinecke committed
121
122
123
124
125
126
127
128
129
                'target': target,
                'harmonic_padding': None,
                'sm_s0': 3.,
                'sm_x0': 1.,
                'key': 'f',
                'causal': causal,
                'minimum_phase': minimum_phase
                }
        model, _ = ift.dynamic_operator(**dct)
Philipp Arras's avatar
Philipp Arras committed
130
        pos = ift.from_random('normal', model.domain)
Martin Reinecke's avatar
Martin Reinecke committed
131
        # FIXME I dont know why smaller tol fails for 3D example
Martin Reinecke's avatar
Martin Reinecke committed
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
        ift.extra.check_jacobian_consistency(model, pos, tol=1e-5, ntries=20)
        if len(target.shape) > 1:
            dct = {
                'target': target,
                'harmonic_padding': None,
                'sm_s0': 3.,
                'sm_x0': 1.,
                'key': 'f',
                'lightcone_key': 'c',
                'sigc': 1.,
                'quant': 5,
                'causal': causal,
                'minimum_phase': minimum_phase
            }
            dct['lightcone_key'] = 'c'
            dct['sigc'] = 1.
            dct['quant'] = 5
            model, _ = ift.dynamic_lightcone_operator(**dct)
Philipp Arras's avatar
Philipp Arras committed
150
            pos = ift.from_random('normal', model.domain)
Martin Reinecke's avatar
Martin Reinecke committed
151
152
153
            # FIXME I dont know why smaller tol fails for 3D example
            ift.extra.check_jacobian_consistency(
                model, pos, tol=1e-5, ntries=20)
Philipp Arras's avatar
Philipp Arras committed
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170


@pmp('h_space', _h_spaces)
@pmp('specialbinbounds', [True, False])
@pmp('logarithmic', [True, False])
@pmp('nbin', [3, None])
def testNormalization(h_space, specialbinbounds, logarithmic, nbin):
    if not specialbinbounds and (not logarithmic or nbin is not None):
        return
    if specialbinbounds:
        binbounds = ift.PowerSpace.useful_binbounds(h_space, logarithmic, nbin)
    else:
        binbounds = None
    dom = ift.PowerSpace(h_space, binbounds)
    op = ift.library.correlated_fields._Normalization(dom)
    pos = 0.1*ift.from_random('normal', op.domain)
    ift.extra.check_jacobian_consistency(op, pos)