Commit 3d526bfa authored by Martin Reinecke's avatar Martin Reinecke
Browse files

Merge branch 'NIFTy_5' into adjust_variances_but_right

parents 926503ff b9f09974
......@@ -73,7 +73,7 @@ if __name__ == '__main__':
# Minimize the Hamiltonian
H = ift.Hamiltonian(likelihood, ic_sampling)
H = ift.EnergyAdapter(position, H)
H = ift.EnergyAdapter(position, H, want_metric=True)
# minimizer = ift.L_BFGS(ic_newton)
H, convergence = minimizer(H)
......
......@@ -93,7 +93,7 @@ if __name__ == '__main__':
# Minimize the Hamiltonian
H = ift.Hamiltonian(likelihood)
H = ift.EnergyAdapter(position, H)
H = ift.EnergyAdapter(position, H, want_metric=True)
H, convergence = minimizer(H)
# Plot results
......
......@@ -91,30 +91,24 @@ if __name__ == '__main__':
# number of samples used to estimate the KL
N_samples = 20
for i in range(2):
metric = H(ift.Linearization.make_var(position)).metric
samples = [
metric.draw_sample(from_inverse=True) for _ in range(N_samples)
]
KL = ift.SampledKullbachLeiblerDivergence(H, samples)
KL = ift.EnergyAdapter(position, KL)
KL = ift.KL_Energy(position, H, N_samples, want_metric=True)
KL, convergence = minimizer(KL)
position = KL.position
plot = ift.Plot()
plot.add(signal(position), title="reconstruction")
plot.add([A(position), A(MOCK_POSITION)], title="power")
plot.add(signal(KL.position), title="reconstruction")
plot.add([A(KL.position), A(MOCK_POSITION)], title="power")
plot.output(ny=1, ysize=6, xsize=16, name="loop.png")
plot = ift.Plot()
sc = ift.StatCalculator()
for sample in samples:
sc.add(signal(sample+position))
for sample in KL.samples:
sc.add(signal(sample+KL.position))
plot.add(sc.mean, title="Posterior Mean")
plot.add(ift.sqrt(sc.var), title="Posterior Standard Deviation")
powers = [A(s+position) for s in samples]
powers = [A(s+KL.position) for s in KL.samples]
plot.add(
[A(position), A(MOCK_POSITION)]+powers,
[A(KL.position), A(MOCK_POSITION)]+powers,
title="Sampled Posterior Power Spectrum")
plot.output(ny=1, nx=3, xsize=24, ysize=6, name="results.png")
......@@ -86,15 +86,16 @@ N = ift.DiagonalOperator(ift.from_global_data(d_space, var))
IC = ift.GradientNormController(tol_abs_gradnorm=1e-8)
likelihood = ift.GaussianEnergy(d, N)(R)
H = ift.Hamiltonian(likelihood, IC)
H = ift.EnergyAdapter(params, H, IC)
Ham = ift.Hamiltonian(likelihood, IC)
H = ift.EnergyAdapter(params, Ham, want_metric=True)
# Minimize
minimizer = ift.NewtonCG(IC)
H, _ = minimizer(H)
# Draw posterior samples
samples = [H.metric.draw_sample(from_inverse=True) + H.position
metric = Ham(ift.Linearization.make_var(H.position, want_metric=True)).metric
samples = [metric.draw_sample(from_inverse=True) + H.position
for _ in range(N_samples)]
# Plotting
......
......@@ -67,6 +67,7 @@ from .minimization.energy import Energy
from .minimization.quadratic_energy import QuadraticEnergy
from .minimization.line_energy import LineEnergy
from .minimization.energy_adapter import EnergyAdapter
from .minimization.kl_energy import KL_Energy
from .sugar import *
from .plotting.plot import Plot
......
......@@ -41,7 +41,7 @@ def _get_acceptable_location(op, loc, lin):
for i in range(50):
try:
loc2 = loc+dir
lin2 = op(Linearization.make_var(loc2))
lin2 = op(Linearization.make_var(loc2, lin.want_metric))
if np.isfinite(lin2.val.sum()) and abs(lin2.val.sum()) < 1e20:
break
except FloatingPointError:
......@@ -54,14 +54,14 @@ def _get_acceptable_location(op, loc, lin):
def _check_consistency(op, loc, tol, ntries, do_metric):
for _ in range(ntries):
lin = op(Linearization.make_var(loc))
lin = op(Linearization.make_var(loc, do_metric))
loc2, lin2 = _get_acceptable_location(op, loc, lin)
dir = loc2-loc
locnext = loc2
dirnorm = dir.norm()
for i in range(50):
locmid = loc + 0.5*dir
linmid = op(Linearization.make_var(locmid))
linmid = op(Linearization.make_var(locmid, do_metric))
dirder = linmid.jac(dir)
numgrad = (lin2.val-lin.val)
xtol = tol * dirder.norm() / np.sqrt(dirder.size)
......
......@@ -53,7 +53,7 @@ class InverseGammaModel(Operator):
outer = 1/outer_inv
jac = makeOp(Field.from_local_data(self._domain, inner*outer))
jac = jac(x.jac)
return Linearization(points, jac)
return x.new(points, jac)
@staticmethod
def IG(field, alpha, q):
......
......@@ -9,13 +9,17 @@ from .sugar import makeOp
class Linearization(object):
def __init__(self, val, jac, metric=None):
def __init__(self, val, jac, metric=None, want_metric=False):
self._val = val
self._jac = jac
if self._val.domain != self._jac.target:
raise ValueError("domain mismatch")
self._want_metric = want_metric
self._metric = metric
def new(self, val, jac, metric=None):
return Linearization(val, jac, metric, self._want_metric)
@property
def domain(self):
return self._jac.domain
......@@ -37,6 +41,10 @@ class Linearization(object):
"""Only available if target is a scalar"""
return self._jac.adjoint_times(Field.scalar(1.))
@property
def want_metric(self):
return self._want_metric
@property
def metric(self):
"""Only available if target is a scalar"""
......@@ -44,35 +52,34 @@ class Linearization(object):
def __getitem__(self, name):
from .operators.simple_linear_operators import FieldAdapter
return Linearization(self._val[name], FieldAdapter(self.domain, name))
return self.new(self._val[name], FieldAdapter(self.domain, name))
def __neg__(self):
return Linearization(
-self._val, -self._jac,
None if self._metric is None else -self._metric)
return self.new(-self._val, -self._jac,
None if self._metric is None else -self._metric)
def conjugate(self):
return Linearization(
return self.new(
self._val.conjugate(), self._jac.conjugate(),
None if self._metric is None else self._metric.conjugate())
@property
def real(self):
return Linearization(self._val.real, self._jac.real)
return self.new(self._val.real, self._jac.real)
def _myadd(self, other, neg):
if isinstance(other, Linearization):
met = None
if self._metric is not None and other._metric is not None:
met = self._metric._myadd(other._metric, neg)
return Linearization(
return self.new(
self._val.flexible_addsub(other._val, neg),
self._jac._myadd(other._jac, neg), met)
if isinstance(other, (int, float, complex, Field, MultiField)):
if neg:
return Linearization(self._val-other, self._jac, self._metric)
return self.new(self._val-other, self._jac, self._metric)
else:
return Linearization(self._val+other, self._jac, self._metric)
return self.new(self._val+other, self._jac, self._metric)
def __add__(self, other):
return self._myadd(other, False)
......@@ -98,7 +105,7 @@ class Linearization(object):
if isinstance(other, Linearization):
if self.target != other.target:
raise ValueError("domain mismatch")
return Linearization(
return self.new(
self._val*other._val,
(makeOp(other._val)(self._jac))._myadd(
makeOp(self._val)(other._jac), False))
......@@ -106,11 +113,11 @@ class Linearization(object):
if other == 1:
return self
met = None if self._metric is None else self._metric.scale(other)
return Linearization(self._val*other, self._jac.scale(other), met)
return self.new(self._val*other, self._jac.scale(other), met)
if isinstance(other, (Field, MultiField)):
if self.target != other.domain:
raise ValueError("domain mismatch")
return Linearization(self._val*other, makeOp(other)(self._jac))
return self.new(self._val*other, makeOp(other)(self._jac))
def __rmul__(self, other):
return self.__mul__(other)
......@@ -118,46 +125,48 @@ class Linearization(object):
def vdot(self, other):
from .operators.simple_linear_operators import VdotOperator
if isinstance(other, (Field, MultiField)):
return Linearization(
return self.new(
Field.scalar(self._val.vdot(other)),
VdotOperator(other)(self._jac))
return Linearization(
return self.new(
Field.scalar(self._val.vdot(other._val)),
VdotOperator(self._val)(other._jac) +
VdotOperator(other._val)(self._jac))
def sum(self):
from .operators.simple_linear_operators import SumReductionOperator
return Linearization(
return self.new(
Field.scalar(self._val.sum()),
SumReductionOperator(self._jac.target)(self._jac))
def exp(self):
tmp = self._val.exp()
return Linearization(tmp, makeOp(tmp)(self._jac))
return self.new(tmp, makeOp(tmp)(self._jac))
def log(self):
tmp = self._val.log()
return Linearization(tmp, makeOp(1./self._val)(self._jac))
return self.new(tmp, makeOp(1./self._val)(self._jac))
def tanh(self):
tmp = self._val.tanh()
return Linearization(tmp, makeOp(1.-tmp**2)(self._jac))
return self.new(tmp, makeOp(1.-tmp**2)(self._jac))
def positive_tanh(self):
tmp = self._val.tanh()
tmp2 = 0.5*(1.+tmp)
return Linearization(tmp2, makeOp(0.5*(1.-tmp**2))(self._jac))
return self.new(tmp2, makeOp(0.5*(1.-tmp**2))(self._jac))
def add_metric(self, metric):
return Linearization(self._val, self._jac, metric)
return self.new(self._val, self._jac, metric)
@staticmethod
def make_var(field):
def make_var(field, want_metric=False):
from .operators.scaling_operator import ScalingOperator
return Linearization(field, ScalingOperator(1., field.domain))
return Linearization(field, ScalingOperator(1., field.domain),
want_metric=want_metric)
@staticmethod
def make_const(field):
def make_const(field, want_metric=False):
from .operators.simple_linear_operators import NullOperator
return Linearization(field, NullOperator(field.domain, field.domain))
return Linearization(field, NullOperator(field.domain, field.domain),
want_metric=want_metric)
......@@ -75,7 +75,7 @@ class ConjugateGradient(Minimizer):
return energy, controller.CONVERGED
while True:
q = energy.metric(d)
q = energy.apply_metric(d)
ddotq = d.vdot(q).real
if ddotq == 0.:
logger.error("Error: ConjugateGradient: ddotq==0.")
......
......@@ -180,7 +180,7 @@ class NewtonCG(DescentMinimizer):
while True:
if abs(ri).sum() <= termcond:
return xsupi
Ap = energy.metric(psupi)
Ap = energy.apply_metric(psupi)
# check curvature
curv = psupi.vdot(Ap)
if 0 <= curv <= 3*float64eps:
......
......@@ -109,6 +109,20 @@ class Energy(NiftyMetaBase()):
"""
raise NotImplementedError
def apply_metric(self, x):
"""
Parameters
----------
x: Field/MultiField
Argument for the metric operator
Returns
-------
Field/MultiField:
Output of the metric operator
"""
raise NotImplementedError
def longest_step(self, dir):
"""Returns the longest allowed step size along `dir`
......
......@@ -8,58 +8,38 @@ from ..operators.scaling_operator import ScalingOperator
class EnergyAdapter(Energy):
def __init__(self, position, op, controller=None, preconditioner=None,
constants=[]):
def __init__(self, position, op, constants=[], want_metric=False):
super(EnergyAdapter, self).__init__(position)
self._op = op
self._val = self._grad = self._metric = None
self._controller = controller
self._preconditioner = preconditioner
self._constants = constants
def at(self, position):
return EnergyAdapter(position, self._op, self._controller,
self._preconditioner, self._constants)
def _fill_all(self):
self._want_metric = want_metric
if len(self._constants) == 0:
tmp = self._op(Linearization.make_var(self._position))
tmp = self._op(Linearization.make_var(self._position, want_metric))
else:
ops = [ScalingOperator(0. if key in self._constants else 1., dom)
for key, dom in self._position.domain.items()]
bdop = BlockDiagonalOperator(self._position.domain, tuple(ops))
tmp = self._op(Linearization(self._position, bdop))
tmp = self._op(Linearization(self._position, bdop,
want_metric=want_metric))
self._val = tmp.val.local_data[()]
self._grad = tmp.gradient
if self._controller is not None:
from ..operators.linear_operator import LinearOperator
from ..operators.inversion_enabler import InversionEnabler
self._metric = tmp._metric
if self._preconditioner is None:
precond = None
elif isinstance(self._preconditioner, LinearOperator):
precond = self._preconditioner
elif isinstance(self._preconditioner, Energy):
precond = self._preconditioner.at(self._position).metric
self._metric = InversionEnabler(tmp._metric, self._controller,
precond)
else:
self._metric = tmp._metric
def at(self, position):
return EnergyAdapter(position, self._op, self._constants,
self._want_metric)
@property
def value(self):
if self._val is None:
self._val = self._op(self._position).local_data[()]
return self._val
@property
def gradient(self):
if self._grad is None:
self._fill_all()
return self._grad
@property
def metric(self):
if self._metric is None:
self._fill_all()
return self._metric
def apply_metric(self, x):
return self._metric(x)
from __future__ import absolute_import, division, print_function
from ..compat import *
from .energy import Energy
from ..linearization import Linearization
from ..operators.scaling_operator import ScalingOperator
from ..operators.block_diagonal_operator import BlockDiagonalOperator
from .. import utilities
class KL_Energy(Energy):
def __init__(self, position, h, nsamp, constants=[], _samples=None,
want_metric=False):
super(KL_Energy, self).__init__(position)
self._h = h
self._constants = constants
self._want_metric = want_metric
if _samples is None:
met = h(Linearization.make_var(position, True)).metric
_samples = tuple(met.draw_sample(from_inverse=True)
for _ in range(nsamp))
self._samples = _samples
if len(constants) == 0:
tmp = Linearization.make_var(position, want_metric)
else:
ops = [ScalingOperator(0. if key in constants else 1., dom)
for key, dom in position.domain.items()]
bdop = BlockDiagonalOperator(position.domain, tuple(ops))
tmp = Linearization(position, bdop, want_metric=want_metric)
mymap = map(lambda v: self._h(tmp+v), self._samples)
tmp = utilities.my_sum(mymap) * (1./len(self._samples))
self._val = tmp.val.local_data[()]
self._grad = tmp.gradient
self._metric = tmp.metric
def at(self, position):
return KL_Energy(position, self._h, 0, self._constants, self._samples,
self._want_metric)
@property
def value(self):
return self._val
@property
def gradient(self):
return self._grad
def apply_metric(self, x):
return self._metric(x)
@property
def metric(self):
return self._metric
@property
def samples(self):
return self._samples
......@@ -77,3 +77,6 @@ class QuadraticEnergy(Energy):
@property
def metric(self):
return self._A
def apply_metric(self, x):
return self._A(x)
......@@ -93,7 +93,7 @@ class _MinHelper(object):
def hessp(self, x, p):
self._update(x)
res = self._energy.metric(_toField(p, self._energy.position))
res = self._energy.apply_metric(_toField(p, self._energy.position))
return _toArray_rw(res)
......
......@@ -42,7 +42,7 @@ class SquaredNormOperator(EnergyOperator):
if isinstance(x, Linearization):
val = Field.scalar(x.val.vdot(x.val))
jac = VdotOperator(2*x.val)(x.jac)
return Linearization(val, jac)
return x.new(val, jac)
return Field.scalar(x.vdot(x))
......@@ -59,7 +59,7 @@ class QuadraticFormOperator(EnergyOperator):
t1 = self._op(x.val)
jac = VdotOperator(t1)(x.jac)
val = Field.scalar(0.5*x.val.vdot(t1))
return Linearization(val, jac)
return x.new(val, jac)
return Field.scalar(0.5*x.vdot(self._op(x)))
......@@ -91,7 +91,7 @@ class GaussianEnergy(EnergyOperator):
def apply(self, x):
residual = x if self._mean is None else x-self._mean
res = self._op(residual).real
if not isinstance(x, Linearization):
if not isinstance(x, Linearization) or not x.want_metric:
return res
metric = SandwichOperator.make(x.jac, self._icov)
return res.add_metric(metric)
......@@ -107,6 +107,8 @@ class PoissonianEnergy(EnergyOperator):
res = x.sum() - x.log().vdot(self._d)
if not isinstance(x, Linearization):
return Field.scalar(res)
if not x.want_metric:
return res
metric = SandwichOperator.make(x.jac, makeOp(1./x.val))
return res.add_metric(metric)
......@@ -136,6 +138,8 @@ class BernoulliEnergy(EnergyOperator):
v = x.log().vdot(-self._d) - (1.-x).log().vdot(1.-self._d)
if not isinstance(x, Linearization):
return Field.scalar(v)
if not x.want_metric:
return v
met = makeOp(1./(x.val*(1.-x.val)))
met = SandwichOperator.make(x.jac, met)
return v.add_metric(met)
......@@ -149,11 +153,11 @@ class Hamiltonian(EnergyOperator):
self._domain = lh.domain
def apply(self, x):
if self._ic_samp is None or not isinstance(x, Linearization):
if (self._ic_samp is None or not isinstance(x, Linearization) or
not x.want_metric):
return self._lh(x)+self._prior(x)
else:
lhx = self._lh(x)
prx = self._prior(x)
lhx, prx = self._lh(x), self._prior(x)
mtr = SamplingEnabler(lhx.metric, prx.metric.inverse,
self._ic_samp, prx.metric.inverse)
return (lhx+prx).add_metric(mtr)
......
......@@ -175,7 +175,7 @@ class LinearOperator(Operator):
return self.apply(x, self.TIMES)
from ..linearization import Linearization
if isinstance(x, Linearization):
return Linearization(self(x._val), self(x._jac))
return x.new(self(x._val), self(x._jac))
return self.__matmul__(x)
def times(self, x):
......
......@@ -144,11 +144,12 @@ class _OpProd(Operator):
v2 = v.extract(self._op2.domain)
if not lin:
return self._op1(v1) * self._op2(v2)
lin1 = self._op1(Linearization.make_var(v1))
lin2 = self._op2(Linearization.make_var(v2))
wm = x.want_metric
lin1 = self._op1(Linearization.make_var(v1, wm))
lin2 = self._op2(Linearization.make_var(v2, wm))
op = (makeOp(lin1._val)(lin2._jac))._myadd(
makeOp(lin2._val)(lin1._jac), False)
return Linearization(lin1._val*lin2._val, op(x.jac))
return lin1.new(lin1._val*lin2._val, op(x.jac))
class _OpSum(Operator):
......@@ -168,10 +169,11 @@ class _OpSum(Operator):
res = None
if not lin:
return self._op1(v1).unite(self._op2(v2))
lin1 = self._op1(Linearization.make_var(v1))
lin2 = self._op2(Linearization.make_var(v2))
wm = x.want_metric
lin1 = self._op1(Linearization.make_var(v1, wm))
lin2 = self._op2(Linearization.make_var(v2, wm))
op = lin1._jac._myadd(lin2._jac, False)
res = Linearization(lin1._val+lin2._val, op(x.jac))
res = lin1.new(lin1._val+lin2._val, op(x.jac))
if lin1._metric is not None and lin2._metric is not None:
res = res.add_metric(lin1._metric + lin2._metric)
return res
......@@ -267,7 +267,6 @@ class Plot(object):
self._plots = []
self._kwargs = []
def add(self, f, **kwargs):
"""Add a figure to the current list of plots.
......@@ -303,7 +302,6 @@ class Plot(object):
self._plots.append(f)
self._kwargs.append(kwargs)
def output(self, **kwargs):
"""Plot the accumulated list of figures.
......
......@@ -113,6 +113,12 @@ class Test_Minimizers(unittest.TestCase):
iteration_limit=1000)