From 347a5b772aaebbe85dda7d0dbcd0404f2f6efe7f Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Fri, 26 Jan 2018 10:28:01 +0100
Subject: [PATCH] more scipy work
---
ci/install_basics.sh | 4 +-
nifty4/__init__.py | 1 +
nifty4/data_objects/distributed_do.py | 7 +-
nifty4/data_objects/numpy_do.py | 4 +
nifty4/dobj.py | 2 +-
nifty4/minimization/scipy_minimizer.py | 109 ++++++++++++++++++++++
test/test_minimization/test_minimizers.py | 25 +++--
7 files changed, 140 insertions(+), 12 deletions(-)
create mode 100644 nifty4/minimization/scipy_minimizer.py
diff --git a/ci/install_basics.sh b/ci/install_basics.sh
index ba35c23d4..d58cbbe55 100644
--- a/ci/install_basics.sh
+++ b/ci/install_basics.sh
@@ -1,3 +1,3 @@
apt-get install -y build-essential git autoconf libtool pkg-config libfftw3-dev openmpi-bin libopenmpi-dev \
- python python-pip python-dev python-nose python-numpy python-matplotlib python-future python-mpi4py \
- python3 python3-pip python3-dev python3-nose python3-numpy python3-matplotlib python3-future python3-mpi4py
+ python python-pip python-dev python-nose python-numpy python-matplotlib python-future python-mpi4py python-scipy \
+ python3 python3-pip python3-dev python3-nose python3-numpy python3-matplotlib python3-future python3-mpi4py python3-scipy
diff --git a/nifty4/__init__.py b/nifty4/__init__.py
index c0df5e303..0cb58e806 100644
--- a/nifty4/__init__.py
+++ b/nifty4/__init__.py
@@ -44,6 +44,7 @@ from .minimization.descent_minimizer import DescentMinimizer
from .minimization.steepest_descent import SteepestDescent
from .minimization.vl_bfgs import VL_BFGS
from .minimization.relaxed_newton import RelaxedNewton
+from .minimization.scipy_minimizer import NewtonCG, L_BFGS_B
from .minimization.energy import Energy
from .minimization.quadratic_energy import QuadraticEnergy
from .minimization.line_energy import LineEnergy
diff --git a/nifty4/data_objects/distributed_do.py b/nifty4/data_objects/distributed_do.py
index c568207f9..c4dbd3172 100644
--- a/nifty4/data_objects/distributed_do.py
+++ b/nifty4/data_objects/distributed_do.py
@@ -27,6 +27,10 @@ rank = _comm.Get_rank()
master = (rank == 0)
+def is_numpy():
+ return False
+
+
def mprint(*args):
if master:
print(*args)
@@ -259,6 +263,7 @@ class data_object(object):
def fill(self, value):
self._data.fill(value)
+
def full(shape, fill_value, dtype=None, distaxis=0):
return data_object(shape, np.full(local_shape(shape, distaxis),
fill_value, dtype), distaxis)
@@ -346,7 +351,7 @@ def local_data(arr):
def ibegin_from_shape(glob_shape, distaxis=0):
res = [0] * len(glob_shape)
- if distaxis<0:
+ if distaxis < 0:
return res
res[distaxis] = _shareRange(glob_shape[distaxis], ntask, rank)[0]
return tuple(res)
diff --git a/nifty4/data_objects/numpy_do.py b/nifty4/data_objects/numpy_do.py
index 189ca40f0..7eeb0eaa1 100644
--- a/nifty4/data_objects/numpy_do.py
+++ b/nifty4/data_objects/numpy_do.py
@@ -30,6 +30,10 @@ rank = 0
master = True
+def is_numpy():
+ return True
+
+
def mprint(*args):
print(*args)
diff --git a/nifty4/dobj.py b/nifty4/dobj.py
index 2aa0eebf9..4d1375970 100644
--- a/nifty4/dobj.py
+++ b/nifty4/dobj.py
@@ -33,4 +33,4 @@ __all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
"log", "tanh", "sqrt", "from_object", "from_random",
"local_data", "ibegin", "ibegin_from_shape", "np_allreduce_sum",
"distaxis", "from_local_data", "from_global_data", "to_global_data",
- "redistribute", "default_distaxis", "mprint"]
+ "redistribute", "default_distaxis", "mprint", "is_numpy"]
diff --git a/nifty4/minimization/scipy_minimizer.py b/nifty4/minimization/scipy_minimizer.py
new file mode 100644
index 000000000..86065c582
--- /dev/null
+++ b/nifty4/minimization/scipy_minimizer.py
@@ -0,0 +1,109 @@
+# 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) 2013-2017 Max-Planck-Society
+#
+# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
+# and financially supported by the Studienstiftung des deutschen Volkes.
+
+from __future__ import division
+from .minimizer import Minimizer
+from ..field import Field
+from .. import dobj
+
+
+class ScipyMinimizer(Minimizer):
+ """Scipy-based minimizer
+
+ Parameters
+ ----------
+ controller : IterationController
+ Object that decides when to terminate the minimization.
+ method : str
+ The selected Scipy minimization method.
+ options : dictionary
+ A set of custom options for the selected minimizer.
+ """
+
+ def __init__(self, controller, method, options, need_hessp):
+ super(ScipyMinimizer, self).__init__()
+ if not dobj.is_numpy():
+ raise NotImplementedError
+ self._controller = controller
+ self._method = method
+ self._options = options
+ self._need_hessp = need_hessp
+
+ def __call__(self, energy):
+ class _MinimizationDone:
+ pass
+
+ class _MinHelper(object):
+ def __init__(self, controller, energy):
+ self._controller = controller
+ self._energy = energy
+ self._domain = energy.position.domain
+
+ def _update(self, x):
+ pos = Field(self._domain, x.reshape(self._domain.shape))
+ if (pos.val != self._energy.position.val).any():
+ self._energy = self._energy.at(pos)
+ status = self._controller.check(self._energy)
+ if status != self._controller.CONTINUE:
+ raise _MinimizationDone
+
+ def fun(self, x):
+ self._update(x)
+ return self._energy.value
+
+ def jac(self, x):
+ self._update(x)
+ return self._energy.gradient.val.reshape(-1)
+
+ def hessp(self, x, p):
+ self._update(x)
+ vec = Field(self._domain, p.reshape(self._domain.shape))
+ res = self._energy.curvature(vec)
+ return res.val.reshape(-1)
+
+ import scipy.optimize as opt
+ hlp = _MinHelper(self._controller, energy)
+ energy = None
+ status = self._controller.start(hlp._energy)
+ if status != self._controller.CONTINUE:
+ return hlp._energy, status
+ try:
+ if self._need_hessp:
+ opt.minimize(hlp.fun, hlp._energy.position.val.reshape(-1),
+ method=self._method, jac=hlp.jac,
+ hessp=hlp.hessp,
+ options=self._options)
+ else:
+ opt.minimize(hlp.fun, hlp._energy.position.val.reshape(-1),
+ method=self._method, jac=hlp.jac,
+ options=self._options)
+ except _MinimizationDone:
+ status = self._controller.check(hlp._energy)
+ return hlp._energy, self._controller.check(hlp._energy)
+ return hlp._energy, self._controller.ERROR
+
+
+def NewtonCG(controller):
+ return ScipyMinimizer(controller, "Newton-CG",
+ {"xtol": 1e-20, "maxiter": None}, True)
+
+
+def L_BFGS_B(controller, maxcor=10):
+ return ScipyMinimizer(controller, "L-BFGS-B",
+ {"ftol": 1e-20, "gtol": 1e-20, "maxcor": maxcor},
+ False)
diff --git a/test/test_minimization/test_minimizers.py b/test/test_minimization/test_minimizers.py
index b7e5b1af0..30b54e334 100644
--- a/test/test_minimization/test_minimizers.py
+++ b/test/test_minimization/test_minimizers.py
@@ -18,14 +18,16 @@
import unittest
import numpy as np
-from numpy.testing import assert_allclose
+from numpy.testing import assert_allclose, assert_equal
import nifty4 as ift
from itertools import product
from test.common import expand
+from nose.plugins.skip import SkipTest
spaces = [ift.RGSpace([1024], distances=0.123), ift.HPSpace(32)]
minimizers = [ift.SteepestDescent, ift.RelaxedNewton, ift.VL_BFGS,
- ift.ConjugateGradient, ift.NonlinearCG]
+ ift.ConjugateGradient, ift.NonlinearCG,
+ ift.NewtonCG, ift.L_BFGS_B]
class Test_Minimizers(unittest.TestCase):
@@ -39,13 +41,20 @@ class Test_Minimizers(unittest.TestCase):
covariance = ift.DiagonalOperator(covariance_diagonal)
required_result = ift.Field.ones(space, dtype=np.float64)
- IC = ift.GradientNormController(tol_abs_gradnorm=1e-5)
- minimizer = minimizer_class(controller=IC)
- energy = ift.QuadraticEnergy(A=covariance, b=required_result,
- position=starting_point)
+ IC = ift.GradientNormController(verbose=True,tol_abs_gradnorm=1e-5, iteration_limit=1000)
+ try:
+ minimizer = minimizer_class(controller=IC)
+ energy = ift.QuadraticEnergy(A=covariance, b=required_result,
+ position=starting_point)
- (energy, convergence) = minimizer(energy)
- assert convergence == IC.CONVERGED
+ (energy, convergence) = minimizer(energy)
+ except NotImplementedError:
+ raise SkipTest
+
+ assert_equal(convergence, IC.CONVERGED)
assert_allclose(ift.dobj.to_global_data(energy.position.val),
1./ift.dobj.to_global_data(covariance_diagonal.val),
rtol=1e-3, atol=1e-3)
+
+
+#MR FIXME: add Rosenbrock test
--
GitLab