Merge branch 'nan_to_inf' into 'NIFTy_6'

Add possibility to cast NaNs to Infs in KL

See merge request !488

nifty6/minimization/metric_gaussian_kl.py

@@ -114,6 +114,11 @@ class MetricGaussianKL(Energy):
         If not None, samples will be distributed as evenly as possible
         across this communicator. If `mirror_samples` is set, then a sample and
         its mirror image will always reside on the same task.
+    nanisinf : bool
+        If true, nan energies which can happen due to overflows in the forward
+        model are interpreted as inf. Thereby, the code does not crash on
+        these occaisions but rather the minimizer is told that the position it
+        has tried is not sensible.
     _local_samples : None
         Only a parameter for internal uses. Typically not to be set by users.
 
@@ -131,7 +136,8 @@ class MetricGaussianKL(Energy):
 
     def __init__(self, mean, hamiltonian, n_samples, constants=[],
                  point_estimates=[], mirror_samples=False,
-                 napprox=0, comm=None, _local_samples=None):
+                 napprox=0, comm=None, _local_samples=None,
+                 nanisinf=False):
         super(MetricGaussianKL, self).__init__(mean)
 
         if not isinstance(hamiltonian, StandardHamiltonian):
@@ -142,6 +148,7 @@ class MetricGaussianKL(Energy):
             raise TypeError
         self._constants = tuple(constants)
         self._point_estimates = tuple(point_estimates)
+        self._mitigate_nans = nanisinf
         if not isinstance(mirror_samples, bool):
             raise TypeError
 
@@ -195,6 +202,8 @@ class MetricGaussianKL(Energy):
                     v += tmp.val.val
                     g = g + tmp.gradient
         self._val = _np_allreduce_sum(self._comm, v)[()] / self._n_eff_samples
+        if np.isnan(self._val) and self._mitigate_nans:
+            self._val = np.inf
         self._grad = _allreduce_sum_field(self._comm, g) / self._n_eff_samples
         self._metric = None
 
@@ -202,7 +211,7 @@ class MetricGaussianKL(Energy):
         return MetricGaussianKL(
             position, self._hamiltonian, self._n_samples, self._constants,
             self._point_estimates, self._mirror_samples, comm=self._comm,
-            _local_samples=self._local_samples)
+            _local_samples=self._local_samples, nanisinf=self._mitigate_nans)
 
     @property
     def value(self):