def compute_agg_rvecs(rvecs_list, idxs_list, aids_list, maws_list):
"""
Sums and normalizes all rvecs that belong to the same word and the same
annotation id
Example:
>>> from ibeis.model.hots.smk.smk_speed import * # NOQA
>>> from ibeis.model.hots.smk import smk_debug
>>> words, wx_sublist, aids_list, idxs_list, idx2_vec, maws_list = smk_debug.testdata_nonagg_rvec()
>>> rvecs_list = compute_nonagg_rvec_listcomp(words, wx_sublist, idxs_list, idx2_vec)
"""
#assert len(idxs_list) == len(rvecs_list)
# group members of each word by aid, we will collapse these groups
grouptup_list = [clustertool.group_indicies(aids) for aids in aids_list]
# Agg aids
aggaids_list = [tup[0] for tup in grouptup_list]
groupxs_list = [tup[1] for tup in grouptup_list]
# Aggregate vecs that belong to the same aid, for each word
# (weighted aggregation with multi-assign-weights)
aggvecs_list = [
np.vstack([smk_core.aggregate_rvecs(rvecs.take(xs, axis=0), maws.take(xs)) for xs in groupxs])
if len(groupxs) > 0 else
np.empty((0, VEC_DIM), dtype=FLOAT_TYPE)
for rvecs, maws, groupxs in zip(rvecs_list, maws_list, groupxs_list)]
# Agg idxs
aggidxs_list = [[idxs.take(xs) for xs in groupxs]
for idxs, groupxs in zip(idxs_list, groupxs_list)]
aggmaws_list = [np.array([maws.take(xs).prod() for xs in groupxs])
for maws, groupxs in zip(maws_list, groupxs_list)]
return aggvecs_list, aggaids_list, aggidxs_list, aggmaws_list
def compute_agg_rvecs(rvecs_list, idxs_list, aids_list, maws_list):
"""
Sums and normalizes all rvecs that belong to the same word and the same
annotation id
Example:
>>> from ibeis.model.hots.smk.smk_speed import * # NOQA
>>> from ibeis.model.hots.smk import smk_debug
>>> words, wx_sublist, aids_list, idxs_list, idx2_vec, maws_list = smk_debug.testdata_nonagg_rvec()
>>> rvecs_list = compute_nonagg_rvec_listcomp(words, wx_sublist, idxs_list, idx2_vec)
"""
#assert len(idxs_list) == len(rvecs_list)
# group members of each word by aid, we will collapse these groups
grouptup_list = [clustertool.group_indicies(aids) for aids in aids_list]
# Agg aids
aggaids_list = [tup[0] for tup in grouptup_list]
groupxs_list = [tup[1] for tup in grouptup_list]
# Aggregate vecs that belong to the same aid, for each word
# (weighted aggregation with multi-assign-weights)
aggvecs_list = [
np.vstack([
smk_core.aggregate_rvecs(rvecs.take(xs, axis=0), maws.take(xs))
for xs in groupxs
]) if len(groupxs) > 0 else np.empty((0, VEC_DIM), dtype=FLOAT_TYPE)
for rvecs, maws, groupxs in zip(rvecs_list, maws_list, groupxs_list)
]
# Agg idxs
aggidxs_list = [[idxs.take(xs) for xs in groupxs]
for idxs, groupxs in zip(idxs_list, groupxs_list)]
aggmaws_list = [
np.array([maws.take(xs).prod() for xs in groupxs])
for maws, groupxs in zip(maws_list, groupxs_list)
]
return aggvecs_list, aggaids_list, aggidxs_list, aggmaws_list
def compute_data_gamma_(idx2_daid, wx2_rvecs, wx2_aids, wx2_idf,
alpha=3, thresh=0):
"""
Computes gamma normalization scalar for the database annotations
Internals step4
>>> from ibeis.model.hots.smk.smk_index import * # NOQA
>>> from ibeis.model.hots.smk import smk_debug
>>> ibs, annots_df, invindex, wx2_idxs, wx2_idf, wx2_rvecs, wx2_aids = smk_debug.testdata_raw_internals2()
>>> alpha = ibs.cfg.query_cfg.smk_cfg.alpha
>>> thresh = ibs.cfg.query_cfg.smk_cfg.thresh
>>> idx2_daid = invindex.idx2_daid
>>> wx2_idf = wx2_idf
>>> daids = invindex.daids
>>> use_cache = USE_CACHE_GAMMA and False
>>> daid2_gamma = compute_data_gamma_(idx2_daid, wx2_rvecs, wx2_aids, wx2_idf, daids, use_cache=use_cache)
"""
if utool.DEBUG2:
from ibeis.model.hots.smk import smk_debug
smk_debug.rrr()
smk_debug.check_wx2(wx2_rvecs=wx2_rvecs, wx2_aids=wx2_aids)
wx_sublist = pdh.ensure_values(pdh.ensure_index(wx2_rvecs))
if utool.VERBOSE:
print('[smk_index] Compute Gamma alpha=%r, thresh=%r: ' % (alpha, thresh))
mark1, end1_ = utool.log_progress(
'[smk_index] Gamma group (by word): ', len(wx_sublist),
flushfreq=100, writefreq=50, with_totaltime=True)
# Get list of aids and rvecs w.r.t. words
aids_list = pdh.ensure_values_subset(wx2_aids, wx_sublist)
rvecs_list1 = pdh.ensure_values_subset(wx2_rvecs, wx_sublist)
# Group by daids first and then by word index
daid2_wx2_drvecs = utool.ddict(lambda: utool.ddict(list))
for wx, aids, rvecs in zip(wx_sublist, aids_list, rvecs_list1):
group_aids, groupxs = clustertool.group_indicies(aids)
rvecs_group = clustertool.apply_grouping(rvecs, groupxs) # 2.9 ms
for aid, rvecs_ in zip(group_aids, rvecs_group):
daid2_wx2_drvecs[aid][wx] = rvecs_
if utool.VERBOSE:
end1_()
# For every daid, compute its gamma using pregrouped rvecs
# Summation over words for each aid
if utool.VERBOSE:
mark2, end2_ = utool.log_progress(
'[smk_index] Gamma Sum (over daid): ', len(daid2_wx2_drvecs),
flushfreq=100, writefreq=25, with_totaltime=True)
# Get lists w.r.t daids
aid_list = list(daid2_wx2_drvecs.keys())
# list of mappings from words to rvecs foreach daid
# [wx2_aidrvecs_1, ..., wx2_aidrvecs_nDaids,]
_wx2_aidrvecs_list = list(daid2_wx2_drvecs.values())
_aidwxs_iter = (list(wx2_aidrvecs.keys()) for wx2_aidrvecs in _wx2_aidrvecs_list)
aidrvecs_list = [list(wx2_aidrvecs.values()) for wx2_aidrvecs in _wx2_aidrvecs_list]
aididf_list = [[wx2_idf[wx] for wx in aidwxs] for aidwxs in _aidwxs_iter]
#gamma_list = []
if utool.DEBUG2:
try:
for count, (idf_list, rvecs_list) in enumerate(zip(aididf_list, aidrvecs_list)):
assert len(idf_list) == len(rvecs_list), 'one list for each word'
#gamma = smk_core.gamma_summation2(rvecs_list, idf_list, alpha, thresh)
except Exception as ex:
utool.printex(ex)
utool.embed()
raise
gamma_list = [smk_core.gamma_summation2(rvecs_list, idf_list, alpha, thresh)
for idf_list, rvecs_list in zip(aididf_list, aidrvecs_list)]
if WITH_PANDAS:
daid2_gamma = pdh.IntSeries(gamma_list, index=aid_list, name='gamma')
else:
daid2_gamma = dict(zip(aid_list, gamma_list))
if utool.VERBOSE:
end2_()
return daid2_gamma
def compute_data_gamma_(idx2_daid,
wx2_rvecs,
wx2_aids,
wx2_idf,
alpha=3,
thresh=0):
"""
Computes gamma normalization scalar for the database annotations
Internals step4
>>> from ibeis.model.hots.smk.smk_index import * # NOQA
>>> from ibeis.model.hots.smk import smk_debug
>>> ibs, annots_df, invindex, wx2_idxs, wx2_idf, wx2_rvecs, wx2_aids = smk_debug.testdata_raw_internals2()
>>> alpha = ibs.cfg.query_cfg.smk_cfg.alpha
>>> thresh = ibs.cfg.query_cfg.smk_cfg.thresh
>>> idx2_daid = invindex.idx2_daid
>>> wx2_idf = wx2_idf
>>> daids = invindex.daids
>>> use_cache = USE_CACHE_GAMMA and False
>>> daid2_gamma = compute_data_gamma_(idx2_daid, wx2_rvecs, wx2_aids, wx2_idf, daids, use_cache=use_cache)
"""
if utool.DEBUG2:
from ibeis.model.hots.smk import smk_debug
smk_debug.rrr()
smk_debug.check_wx2(wx2_rvecs=wx2_rvecs, wx2_aids=wx2_aids)
wx_sublist = pdh.ensure_values(pdh.ensure_index(wx2_rvecs))
if utool.VERBOSE:
print('[smk_index] Compute Gamma alpha=%r, thresh=%r: ' %
(alpha, thresh))
mark1, end1_ = utool.log_progress(
'[smk_index] Gamma group (by word): ',
len(wx_sublist),
flushfreq=100,
writefreq=50,
with_totaltime=True)
# Get list of aids and rvecs w.r.t. words
aids_list = pdh.ensure_values_subset(wx2_aids, wx_sublist)
rvecs_list1 = pdh.ensure_values_subset(wx2_rvecs, wx_sublist)
# Group by daids first and then by word index
daid2_wx2_drvecs = utool.ddict(lambda: utool.ddict(list))
for wx, aids, rvecs in zip(wx_sublist, aids_list, rvecs_list1):
group_aids, groupxs = clustertool.group_indicies(aids)
rvecs_group = clustertool.apply_grouping(rvecs, groupxs) # 2.9 ms
for aid, rvecs_ in zip(group_aids, rvecs_group):
daid2_wx2_drvecs[aid][wx] = rvecs_
if utool.VERBOSE:
end1_()
# For every daid, compute its gamma using pregrouped rvecs
# Summation over words for each aid
if utool.VERBOSE:
mark2, end2_ = utool.log_progress(
'[smk_index] Gamma Sum (over daid): ',
len(daid2_wx2_drvecs),
flushfreq=100,
writefreq=25,
with_totaltime=True)
# Get lists w.r.t daids
aid_list = list(daid2_wx2_drvecs.keys())
# list of mappings from words to rvecs foreach daid
# [wx2_aidrvecs_1, ..., wx2_aidrvecs_nDaids,]
_wx2_aidrvecs_list = list(daid2_wx2_drvecs.values())
_aidwxs_iter = (list(wx2_aidrvecs.keys())
for wx2_aidrvecs in _wx2_aidrvecs_list)
aidrvecs_list = [
list(wx2_aidrvecs.values()) for wx2_aidrvecs in _wx2_aidrvecs_list
]
aididf_list = [[wx2_idf[wx] for wx in aidwxs] for aidwxs in _aidwxs_iter]
#gamma_list = []
if utool.DEBUG2:
try:
for count, (idf_list, rvecs_list) in enumerate(
zip(aididf_list, aidrvecs_list)):
assert len(idf_list) == len(
rvecs_list), 'one list for each word'
#gamma = smk_core.gamma_summation2(rvecs_list, idf_list, alpha, thresh)
except Exception as ex:
utool.printex(ex)
utool.embed()
raise
gamma_list = [
smk_core.gamma_summation2(rvecs_list, idf_list, alpha, thresh)
for idf_list, rvecs_list in zip(aididf_list, aidrvecs_list)
]
if WITH_PANDAS:
daid2_gamma = pdh.IntSeries(gamma_list, index=aid_list, name='gamma')
else:
daid2_gamma = dict(zip(aid_list, gamma_list))
if utool.VERBOSE:
end2_()
return daid2_gamma
