def asynchronousFinalize(self, batch):
"""
Method updating all global estimators with the contributions of the new batch.
"""
# Postprocess on finished batches
for level in range(len(self.batchIndices[batch])):
# update global estimators
mda.updateGlobalMomentEstimator_Task(
self.indices[level].qoiEstimator,
self.batchIndices[batch][level].qoiEstimator,
self.indices[level].costEstimator,
self.batchIndices[batch][level].costEstimator,
batch,
)
# delete COMPSs future objects no longer needed
delete_object(
self.batchIndices[batch][level].costEstimator,
*self.batchIndices[batch][level].qoiEstimator,
)
# Update model coefficients for cost, bias, variance with new observations
self.updatePredictors()
for level in range(len(self.indices)):
# synchronize estimator needed for checking convergence and updating hierarchy
self.indices[level].qoiEstimator = get_value_from_remote(
self.indices[level].qoiEstimator
)
self.indices[level].costEstimator = get_value_from_remote(
self.indices[level].costEstimator
)
def estimation(self, assemblerCoordinates=None):
"""
For an entry of assembler, collect estimations from all indices corresponding to
the same entry of estimatorsForAssembler. Then, pass this list to the assembleEstimation
method of that entry of assembler.
"""
# If nothing is specified, assemble all estimations
if assemblerCoordinates is None:
assemblerCoordinates = range(len(self.assemblers))
# Extract current hierarchy from list of indices
hierarchy = self.hierarchy()
## Get list of estimations for assemblers without duplicate calls
# List of which assembler needs what estimations
args = [self.estimatorsForAssembler[c] for c in assemblerCoordinates]
# Convert nested list to nested tuple
mapArg = [
((i, j), (v[0], tuple(v[1]))) for i, a in enumerate(args) for j, v in enumerate(a)
]
# Create dictionary of {argument: [coord1, ...], ...}
# such that args[coord1[0]][coord1[1]] = argument
argMap = defaultdict(list)
for t in mapArg:
argMap[t[1]].append(t[0])
# Initialise and fill list of estimations for assemblers
estimations = [[[] for _ in a] for a in args]
for estArgs, coords in argMap.items():
# Compute this unique estimation
est = self.indexEstimation(*estArgs)
# Distribute it wherever is appeared in args
for c in coords:
estimations[c[0]][c[1]] = est
# Run the corresponding estimation methods on this
globalEstimations = []
# Iterate over couples (coord,estimation)
for c, e in zip(assemblerCoordinates, estimations):
ge = self.assemblers[c].assembleEstimation(hierarchy, e)
globalEstimations.append(ge)
# Delete COMPSs objects
# Flatten list of depth 2 then unpack
delete_object(
*chain.from_iterable(hierarchy),
*chain.from_iterable(chain.from_iterable(estimations)),
)
return globalEstimations