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feat: Speed up covariance for irregular MC chains

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This commit is contained in:
Simon Kuberski 2022-10-21 11:05:54 +02:00
parent 50ac82ed1d
commit d5a766ee16
2 changed files with 15 additions and 46 deletions
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57
pyerrors/obs.py
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@ -1097,34 +1097,6 @@ def _expand_deltas_for_merge(deltas, idx, shape, new_idx):
return np.array([ret[new_idx[i] - new_idx[0]] for i in range(len(new_idx))])
def _collapse_deltas_for_merge(deltas, idx, shape, new_idx):
"""Collapse deltas defined on idx to the list of configs that is defined by new_idx.
If idx and new_idx are of type range, the smallest
common divisor of the step sizes is used as new step size.
Parameters
----------
deltas : list
List of fluctuations
idx : list
List or range of configs on which the deltas are defined.
Has to be a subset of new_idx and has to be sorted in ascending order.
shape : list
Number of configs in idx.
new_idx : list
List of configs that defines the new range, has to be sorted in ascending order.
"""
if type(idx) is range and type(new_idx) is range:
if idx == new_idx:
return deltas
ret = np.zeros(new_idx[-1] - new_idx[0] + 1)
for i in range(shape):
if idx[i] in new_idx:
ret[idx[i] - new_idx[0]] = deltas[i]
return np.array([ret[new_idx[i] - new_idx[0]] for i in range(len(new_idx))])
def _filter_zeroes(deltas, idx, eps=Obs.filter_eps):
"""Filter out all configurations with vanishing fluctuation such that they do not
contribute to the error estimate anymore. Returns the new deltas and
@ -1355,20 +1327,17 @@ def _reduce_deltas(deltas, idx_old, idx_new):
if type(idx_old) is range and type(idx_new) is range:
if idx_old == idx_new:
return deltas
shape = len(idx_new)
ret = np.zeros(shape)
oldpos = 0
for i in range(shape):
pos = -1
for j in range(oldpos, len(idx_old)):
if idx_old[j] == idx_new[i]:
pos = j
break
if pos < 0:
raise Exception('Error in _reduce_deltas: Config %d not in idx_old' % (idx_new[i]))
ret[i] = deltas[pos]
oldpos = pos
return np.array(ret)
# Use groupby to efficiently check whether all elements of idx_old and idx_new are identical
try:
g = groupby([idx_old, idx_new])
if next(g, True) and not next(g, False):
return deltas
except Exception:
pass
indices = np.intersect1d(idx_old, idx_new, assume_unique=True, return_indices=True)[1]
if len(indices) < len(idx_new):
raise Exception('Error in _reduce_deltas: Config of idx_new not in idx_old')
return np.array(deltas)[indices]
def reweight(weight, obs, **kwargs):
@ -1546,8 +1515,8 @@ def _covariance_element(obs1, obs2):
"""Estimates the covariance of two Obs objects, neglecting autocorrelations."""
def calc_gamma(deltas1, deltas2, idx1, idx2, new_idx):
deltas1 = _collapse_deltas_for_merge(deltas1, idx1, len(idx1), new_idx)
deltas2 = _collapse_deltas_for_merge(deltas2, idx2, len(idx2), new_idx)
deltas1 = _reduce_deltas(deltas1, idx1, new_idx)
deltas2 = _reduce_deltas(deltas2, idx2, new_idx)
return np.sum(deltas1 * deltas2)
if set(obs1.names).isdisjoint(set(obs2.names)):

4
tests/obs_test.py
View file

@ -534,7 +534,7 @@ def test_merge_intersection():
assert pe.obs._merge_idx(idl_list) == pe.obs._intersection_idx(idl_list)
def test_intersection_collapse():
def test_intersection_reduce():
range1 = range(1, 2000, 2)
range2 = range(2, 2001, 8)
@ -542,7 +542,7 @@ def test_intersection_collapse():
obs_merge = obs1 + pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])
intersection = pe.obs._intersection_idx([o.idl["ens"] for o in [obs1, obs_merge]])
coll = pe.obs._collapse_deltas_for_merge(obs_merge.deltas["ens"], obs_merge.idl["ens"], len(obs_merge.idl["ens"]), range1)
coll = pe.obs._reduce_deltas(obs_merge.deltas["ens"], obs_merge.idl["ens"], range1)
assert np.all(coll == obs1.deltas["ens"])