def OLD_compute_data_sccw_(idx2_daid, wx2_drvecs, wx2_aids, wx2_idf, wx2_dmaws,
smk_alpha, smk_thresh, verbose=False):
"""
"""
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.rrr()
smk_debug.check_wx2(wx2_rvecs=wx2_drvecs, wx2_aids=wx2_aids)
with ut.Timer('timer_orig1'):
wx_sublist = np.array(wx2_drvecs.keys())
if not ut.QUIET:
print('\n[smk_index.sccw] +--- Start Compute Data Self Consistency Weight')
if ut.VERBOSE or verbose:
print('[smk_index.sccw] Compute SCCW smk_alpha=%r, smk_thresh=%r: ' % (smk_alpha, smk_thresh))
mark1, end1_ = ut.log_progress(
'[smk_index.sccw] SCCW group (by present words): ', len(wx_sublist),
freq=100, with_time=WITH_TOTALTIME)
# Get list of aids and rvecs w.r.t. words
aids_list = [wx2_aids[wx] for wx in wx_sublist]
rvecs_list1 = [wx2_drvecs[wx] for wx in wx_sublist]
maws_list = [wx2_dmaws[wx] for wx in wx_sublist]
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.assert_single_assigned_maws(maws_list)
# Group by daids first and then by word index
daid2_wx2_drvecs = clustertool.double_group(wx_sublist, aids_list, rvecs_list1)
if ut.VERBOSE or verbose:
end1_()
# For every daid, compute its sccw using pregrouped rvecs
# Summation over words for each aid
if ut.VERBOSE or verbose:
mark2, end2_ = ut.log_progress(
'[smk_index.sccw] SCCW Sum (over daid): ', len(daid2_wx2_drvecs),
freq=25, with_time=WITH_TOTALTIME)
# 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]
with ut.Timer('timer_orig2'):
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.check_data_smksumm(aididf_list, aidrvecs_list)
# TODO: implement database side soft-assign
sccw_list = [smk_scoring.sccw_summation(rvecs_list, None, idf_list, None, smk_alpha, smk_thresh)
for idf_list, rvecs_list in zip(aididf_list, aidrvecs_list)]
daid2_sccw = dict(zip(aid_list, sccw_list))
if ut.VERBOSE or verbose:
end2_()
print('[smk_index.sccw] L___ End Compute Data SCCW\n')
return daid2_sccw
def OLD_compute_data_sccw_(idx2_daid, wx2_drvecs, wx2_aids, wx2_idf, wx2_dmaws,
smk_alpha, smk_thresh, verbose=False):
"""
"""
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.rrr()
smk_debug.check_wx2(wx2_rvecs=wx2_drvecs, wx2_aids=wx2_aids)
with ut.Timer('timer_orig1'):
wx_sublist = np.array(wx2_drvecs.keys())
if not ut.QUIET:
print('\n[smk_index.sccw] +--- Start Compute Data Self Consistency Weight')
if ut.VERBOSE or verbose:
print('[smk_index.sccw] Compute SCCW smk_alpha=%r, smk_thresh=%r: ' % (smk_alpha, smk_thresh))
# Get list of aids and rvecs w.r.t. words
aids_list = [wx2_aids[wx] for wx in wx_sublist]
rvecs_list1 = [wx2_drvecs[wx] for wx in wx_sublist]
maws_list = [wx2_dmaws[wx] for wx in wx_sublist]
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.assert_single_assigned_maws(maws_list)
# Group by daids first and then by word index
daid2_wx2_drvecs = clustertool.double_group(wx_sublist, aids_list, rvecs_list1)
# For every daid, compute its sccw using pregrouped rvecs
# Summation over words for each aid
if ut.VERBOSE or verbose:
print('[smk_index.sccw] SCCW Sum (over daid): ')
# 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]
with ut.Timer('timer_orig2'):
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.check_data_smksumm(aididf_list, aidrvecs_list)
# TODO: implement database side soft-assign
sccw_list = [smk_scoring.sccw_summation(rvecs_list, None, idf_list, None, smk_alpha, smk_thresh)
for idf_list, rvecs_list in zip(aididf_list, aidrvecs_list)]
daid2_sccw = dict(zip(aid_list, sccw_list))
if ut.VERBOSE or verbose:
print('[smk_index.sccw] L___ End Compute Data SCCW\n')
return daid2_sccw
def compute_data_sccw_(idx2_daid, wx2_drvecs, wx2_dflags, wx2_aids, wx2_idf,
wx2_dmaws, smk_alpha, smk_thresh, verbose=False):
"""
Computes sccw normalization scalar for the database annotations.
This is gamma from the SMK paper.
sccw is a self consistency critiron weight --- a scalar which ensures
the score of K(X, X) = 1
Args:
idx2_daid ():
wx2_drvecs ():
wx2_aids ():
wx2_idf ():
wx2_dmaws ():
smk_alpha ():
smk_thresh ():
Returns:
daid2_sccw
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.hots.smk.smk_index import * # NOQA
>>> from ibeis.algo.hots.smk import smk_index
>>> from ibeis.algo.hots.smk import smk_debug
>>> #tup = smk_debug.testdata_compute_data_sccw(db='testdb1')
>>> tup = smk_debug.testdata_compute_data_sccw(db='PZ_MTEST')
>>> ibs, annots_df, invindex, wx2_idxs, wx2_idf, wx2_drvecs, wx2_aids, qparams = tup
>>> wx2_dflags = invindex.wx2_dflags
>>> ws2_idxs = invindex.wx2_idxs
>>> wx2_dmaws = invindex.wx2_dmaws
>>> idx2_daid = invindex.idx2_daid
>>> daids = invindex.daids
>>> smk_alpha = qparams.smk_alpha
>>> smk_thresh = qparams.smk_thresh
>>> wx2_idf = wx2_idf
>>> verbose = True
>>> invindex.invindex_dbgstr()
>>> invindex.report_memory()
>>> invindex.report_memsize()
>>> daid2_sccw = smk_index.compute_data_sccw_(idx2_daid, wx2_drvecs, wx2_dflags, wx2_aids, wx2_idf, wx2_dmaws, smk_alpha, smk_thresh, verbose)
"""
#for wx in wx_sublist:
# print(len(wx2_dmaws
verbose_ = ut.VERBOSE or verbose
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.check_wx2(wx2_rvecs=wx2_drvecs, wx2_aids=wx2_aids)
if not ut.QUIET:
print('\n[smk_index.sccw] +--- Start Compute Data Self Consistency Weight')
if verbose_:
print('[smk_index.sccw] Compute SCCW smk_alpha=%r, smk_thresh=%r: ' % (smk_alpha, smk_thresh))
mark1, end1_ = ut.log_progress(
'[smk_index.sccw] SCCW group (by present words): ', len(wx2_drvecs),
freq=100, with_time=WITH_TOTALTIME)
# Group by daids first and then by word index
# Get list of aids and rvecs w.r.t. words (ie one item per word)
wx_sublist = np.array(list(wx2_drvecs.keys()))
aids_perword = [wx2_aids[wx] for wx in wx_sublist]
# wx_list1: Lays out word indexes for each annotation
# tx_list1: Temporary within annotation subindex + wx uniquely identifies
# item in wx2_drvecs, wx2_dflags, and wx2_dmaws
# Flatten out indexes to perform grouping
flat_aids = np.hstack(aids_perword)
count = len(flat_aids)
txs_perword = [np.arange(aids.size) for aids in aids_perword]
flat_txs = np.hstack(txs_perword)
# fromiter is faster for flat_wxs because is not a list of numpy arrays
wxs_perword = ([wx] * len(aids) for wx, aids in zip(wx_sublist, aids_perword))
flat_wxs = np.fromiter(ut.iflatten(wxs_perword), hstypes.INDEX_TYPE, count)
# Group flat indexes by annotation id
unique_aids, annot_groupxs = clustertool.group_indices(flat_aids)
# Wxs and Txs grouped by annotation id
wxs_perannot = clustertool.apply_grouping_iter(flat_wxs, annot_groupxs)
txs_perannot = clustertool.apply_grouping_iter(flat_txs, annot_groupxs)
# Group by word inside each annotation group
wxsubgrouping_perannot = [clustertool.group_indices(wxs)
for wxs in wxs_perannot]
word_groupxs_perannot = (groupxs for wxs, groupxs in wxsubgrouping_perannot)
txs_perword_perannot = [clustertool.apply_grouping(txs, groupxs)
for txs, groupxs in
zip(txs_perannot, word_groupxs_perannot)]
wxs_perword_perannot = [wxs for wxs, groupxs in wxsubgrouping_perannot]
# Group relavent data for sccw measure by word for each annotation grouping
def _vector_subgroup_by_wx(wx2_arr, wxs_perword_perannot, txs_perword_perannot):
return [[wx2_arr[wx].take(txs, axis=0)
for wx, txs in zip(wx_perword_, txs_perword_)]
for wx_perword_, txs_perword_ in
zip(wxs_perword_perannot, txs_perword_perannot)]
def _scalar_subgroup_by_wx(wx2_scalar, wxs_perword_perannot):
return [[wx2_scalar[wx] for wx in wxs] for wxs in wxs_perword_perannot]
subgrouped_drvecs = _vector_subgroup_by_wx(wx2_drvecs, wxs_perword_perannot, txs_perword_perannot)
subgrouped_dmaws = _vector_subgroup_by_wx(wx2_dmaws, wxs_perword_perannot, txs_perword_perannot)
# If we aren't using dmaws replace it with an infinite None iterator
#subgrouped_dmaws = iter(lambda: None, 1)
subgrouped_dflags = _vector_subgroup_by_wx(wx2_dflags, wxs_perword_perannot, txs_perword_perannot)
#subgrouped_dflags = iter(lambda: None, 1)
subgrouped_idfs = _scalar_subgroup_by_wx(wx2_idf, wxs_perword_perannot)
if verbose_:
end1_()
mark2, end2_ = ut.log_progress(lbl='[smk_index.sccw] SCCW Sum (over daid): ',
total=len(unique_aids), freq=100, with_time=WITH_TOTALTIME)
progiter = ut.ProgressIter(lbl='[smk_index.sccw] SCCW Sum (over daid): ',
total=len(unique_aids), freq=10, with_time=WITH_TOTALTIME)
else:
progiter = ut.identity
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.check_data_smksumm(subgrouped_idfs, subgrouped_drvecs)
sccw_list = [
smk_scoring.sccw_summation(rvecs_list, flags_list, idf_list, maws_list, smk_alpha, smk_thresh)
for rvecs_list, flags_list, maws_list, idf_list in
progiter(zip(subgrouped_drvecs, subgrouped_dflags, subgrouped_dmaws, subgrouped_idfs))
]
daid2_sccw = dict(zip(unique_aids, sccw_list))
if verbose_:
end2_()
print('[smk_index.sccw] L___ End Compute Data SCCW\n')
return daid2_sccw
def compute_data_sccw_(idx2_daid, wx2_drvecs, wx2_dflags, wx2_aids, wx2_idf,
wx2_dmaws, smk_alpha, smk_thresh, verbose=False):
"""
Computes sccw normalization scalar for the database annotations.
This is gamma from the SMK paper.
sccw is a self consistency critiron weight --- a scalar which ensures
the score of K(X, X) = 1
Args:
idx2_daid ():
wx2_drvecs ():
wx2_aids ():
wx2_idf ():
wx2_dmaws ():
smk_alpha ():
smk_thresh ():
Returns:
daid2_sccw
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.hots.smk.smk_index import * # NOQA
>>> from ibeis.algo.hots.smk import smk_index
>>> from ibeis.algo.hots.smk import smk_debug
>>> #tup = smk_debug.testdata_compute_data_sccw(db='testdb1')
>>> tup = smk_debug.testdata_compute_data_sccw(db='PZ_MTEST')
>>> ibs, annots_df, invindex, wx2_idxs, wx2_idf, wx2_drvecs, wx2_aids, qparams = tup
>>> wx2_dflags = invindex.wx2_dflags
>>> ws2_idxs = invindex.wx2_idxs
>>> wx2_dmaws = invindex.wx2_dmaws
>>> idx2_daid = invindex.idx2_daid
>>> daids = invindex.daids
>>> smk_alpha = qparams.smk_alpha
>>> smk_thresh = qparams.smk_thresh
>>> wx2_idf = wx2_idf
>>> verbose = True
>>> invindex.invindex_dbgstr()
>>> invindex.report_memory()
>>> invindex.report_memsize()
>>> daid2_sccw = smk_index.compute_data_sccw_(idx2_daid, wx2_drvecs, wx2_dflags, wx2_aids, wx2_idf, wx2_dmaws, smk_alpha, smk_thresh, verbose)
"""
#for wx in wx_sublist:
# print(len(wx2_dmaws
verbose_ = ut.VERBOSE or verbose
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.check_wx2(wx2_rvecs=wx2_drvecs, wx2_aids=wx2_aids)
if not ut.QUIET:
print('\n[smk_index.sccw] +--- Start Compute Data Self Consistency Weight')
if verbose_:
print('[smk_index.sccw] Compute SCCW smk_alpha=%r, smk_thresh=%r: ' % (smk_alpha, smk_thresh))
# Group by daids first and then by word index
# Get list of aids and rvecs w.r.t. words (ie one item per word)
wx_sublist = np.array(list(wx2_drvecs.keys()))
aids_perword = [wx2_aids[wx] for wx in wx_sublist]
# wx_list1: Lays out word indexes for each annotation
# tx_list1: Temporary within annotation subindex + wx uniquely identifies
# item in wx2_drvecs, wx2_dflags, and wx2_dmaws
# Flatten out indexes to perform grouping
flat_aids = np.hstack(aids_perword)
count = len(flat_aids)
txs_perword = [np.arange(aids.size) for aids in aids_perword]
flat_txs = np.hstack(txs_perword)
# fromiter is faster for flat_wxs because is not a list of numpy arrays
wxs_perword = ([wx] * len(aids) for wx, aids in zip(wx_sublist, aids_perword))
flat_wxs = np.fromiter(ut.iflatten(wxs_perword), hstypes.INDEX_TYPE, count)
# Group flat indexes by annotation id
unique_aids, annot_groupxs = clustertool.group_indices(flat_aids)
# Wxs and Txs grouped by annotation id
wxs_perannot = clustertool.apply_grouping_iter(flat_wxs, annot_groupxs)
txs_perannot = clustertool.apply_grouping_iter(flat_txs, annot_groupxs)
# Group by word inside each annotation group
wxsubgrouping_perannot = [clustertool.group_indices(wxs)
for wxs in wxs_perannot]
word_groupxs_perannot = (groupxs for wxs, groupxs in wxsubgrouping_perannot)
txs_perword_perannot = [clustertool.apply_grouping(txs, groupxs)
for txs, groupxs in
zip(txs_perannot, word_groupxs_perannot)]
wxs_perword_perannot = [wxs for wxs, groupxs in wxsubgrouping_perannot]
# Group relavent data for sccw measure by word for each annotation grouping
def _vector_subgroup_by_wx(wx2_arr, wxs_perword_perannot, txs_perword_perannot):
return [[wx2_arr[wx].take(txs, axis=0)
for wx, txs in zip(wx_perword_, txs_perword_)]
for wx_perword_, txs_perword_ in
zip(wxs_perword_perannot, txs_perword_perannot)]
def _scalar_subgroup_by_wx(wx2_scalar, wxs_perword_perannot):
return [[wx2_scalar[wx] for wx in wxs] for wxs in wxs_perword_perannot]
subgrouped_drvecs = _vector_subgroup_by_wx(wx2_drvecs, wxs_perword_perannot, txs_perword_perannot)
subgrouped_dmaws = _vector_subgroup_by_wx(wx2_dmaws, wxs_perword_perannot, txs_perword_perannot)
# If we aren't using dmaws replace it with an infinite None iterator
#subgrouped_dmaws = iter(lambda: None, 1)
subgrouped_dflags = _vector_subgroup_by_wx(wx2_dflags, wxs_perword_perannot, txs_perword_perannot)
#subgrouped_dflags = iter(lambda: None, 1)
subgrouped_idfs = _scalar_subgroup_by_wx(wx2_idf, wxs_perword_perannot)
if verbose_:
progiter = ut.ProgressIter(lbl='[smk_index.sccw] SCCW Sum (over daid): ',
total=len(unique_aids), freq=10, with_time=WITH_TOTALTIME)
else:
progiter = ut.identity
if ut.DEBUG2:
from ibeis.algo.hots.smk import smk_debug
smk_debug.check_data_smksumm(subgrouped_idfs, subgrouped_drvecs)
sccw_list = [
smk_scoring.sccw_summation(rvecs_list, flags_list, idf_list, maws_list, smk_alpha, smk_thresh)
for rvecs_list, flags_list, maws_list, idf_list in
progiter(zip(subgrouped_drvecs, subgrouped_dflags, subgrouped_dmaws, subgrouped_idfs))
]
daid2_sccw = dict(zip(unique_aids, sccw_list))
if verbose_:
print('[smk_index.sccw] L___ End Compute Data SCCW\n')
return daid2_sccw
