def compute_agg_rvecs(rvecs_list, idxs_list, aids_list, maws_list):
"""
Driver function for agg residual computation
Sums and normalizes all rvecs that belong to the same word and the same
annotation id
Args:
rvecs_list (list): residual vectors grouped by word
idxs_list (list): stacked descriptor indexes grouped by word
aids_list (list): annotation rowid for each stacked descriptor index
maws_list (list): multi assign weights
Returns:
tuple : (aggvecs_list, aggaids_list, aggidxs_list, aggmaws_list)
CommandLine:
python -m ibeis.algo.hots.smk.smk_residuals --test-compute_agg_rvecs
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.hots.smk.smk_residuals import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> from ibeis.algo.hots.smk import smk_residuals
>>> words, wx_sublist, aids_list, idxs_list, idx2_vec, maws_list = smk_debug.testdata_nonagg_rvec()
>>> rvecs_list, flags_list = smk_residuals.compute_nonagg_rvecs(words, idx2_vec, wx_sublist, idxs_list)
>>> tup = compute_agg_rvecs(rvecs_list, idxs_list, aids_list, maws_list)
>>> aggvecs_list, aggaids_list, aggidxs_list, aggmaws_list, aggflags_list = tup
>>> ut.assert_eq(len(wx_sublist), len(rvecs_list))
"""
#assert len(idxs_list) == len(rvecs_list)
# group members of each word by aid, we will collapse these groups
grouptup_list = [clustertool.group_indices(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([
aggregate_rvecs(rvecs.take(xs, axis=0), maws.take(xs))
for xs in groupxs
]) if len(groupxs) > 0 else np.empty(
(0, hstypes.VEC_DIM), dtype=hstypes.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)
]
# Need to recompute flags for consistency
# flag is true when aggvec is all zeros
aggflags_list = [~np.any(aggvecs, axis=1) for aggvecs in aggvecs_list]
return aggvecs_list, aggaids_list, aggidxs_list, aggmaws_list, aggflags_list
def group_correspondences(all_matches, all_scores, all_daids, daid2_sccw):
daid_keys, groupxs = clustertool.group_indices(all_daids)
fs_list = clustertool.apply_grouping(all_scores, groupxs)
fm_list = clustertool.apply_grouping(all_matches, groupxs)
daid2_fm = {daid: fm for daid, fm in zip(daid_keys, fm_list)}
daid2_fs = {daid: fs * daid2_sccw[daid] for daid, fs in zip(daid_keys, fs_list)}
# FIXME: generalize to when nAssign > 1
daid2_fk = {daid: np.ones(fs.size, dtype=hstypes.FK_DTYPE) for daid, fs in zip(daid_keys, fs_list)}
daid2_chipmatch = (daid2_fm, daid2_fs, daid2_fk)
return daid2_chipmatch
def compute_agg_rvecs(rvecs_list, idxs_list, aids_list, maws_list):
"""
Driver function for agg residual computation
Sums and normalizes all rvecs that belong to the same word and the same
annotation id
Args:
rvecs_list (list): residual vectors grouped by word
idxs_list (list): stacked descriptor indexes grouped by word
aids_list (list): annotation rowid for each stacked descriptor index
maws_list (list): multi assign weights
Returns:
tuple : (aggvecs_list, aggaids_list, aggidxs_list, aggmaws_list)
CommandLine:
python -m ibeis.algo.hots.smk.smk_residuals --test-compute_agg_rvecs
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.hots.smk.smk_residuals import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> from ibeis.algo.hots.smk import smk_residuals
>>> words, wx_sublist, aids_list, idxs_list, idx2_vec, maws_list = smk_debug.testdata_nonagg_rvec()
>>> rvecs_list, flags_list = smk_residuals.compute_nonagg_rvecs(words, idx2_vec, wx_sublist, idxs_list)
>>> tup = compute_agg_rvecs(rvecs_list, idxs_list, aids_list, maws_list)
>>> aggvecs_list, aggaids_list, aggidxs_list, aggmaws_list, aggflags_list = tup
>>> ut.assert_eq(len(wx_sublist), len(rvecs_list))
"""
#assert len(idxs_list) == len(rvecs_list)
# group members of each word by aid, we will collapse these groups
grouptup_list = [clustertool.group_indices(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([aggregate_rvecs(rvecs.take(xs, axis=0), maws.take(xs)) for xs in groupxs])
if len(groupxs) > 0 else
np.empty((0, hstypes.VEC_DIM), dtype=hstypes.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)]
# Need to recompute flags for consistency
# flag is true when aggvec is all zeros
aggflags_list = [~np.any(aggvecs, axis=1) for aggvecs in aggvecs_list]
return aggvecs_list, aggaids_list, aggidxs_list, aggmaws_list, aggflags_list
def group_correspondences(all_matches, all_scores, all_daids, daid2_sccw):
daid_keys, groupxs = clustertool.group_indices(all_daids)
fs_list = clustertool.apply_grouping(all_scores, groupxs)
fm_list = clustertool.apply_grouping(all_matches, groupxs)
daid2_fm = {daid: fm for daid, fm in zip(daid_keys, fm_list)}
daid2_fs = {
daid: fs * daid2_sccw[daid]
for daid, fs in zip(daid_keys, fs_list)
}
# FIXME: generalize to when nAssign > 1
daid2_fk = {
daid: np.ones(fs.size, dtype=hstypes.FK_DTYPE)
for daid, fs in zip(daid_keys, fs_list)
}
daid2_chipmatch = (daid2_fm, daid2_fs, daid2_fk)
return daid2_chipmatch
def compute_idf_label1(aids_list, daid2_label):
"""
One of our idf extensions
Example:
>>> from ibeis.algo.hots.smk.smk_index import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, invindex, wx2_idxs, qparams = smk_debug.testdata_raw_internals1()
>>> wx_series = np.arange(len(invindex.words))
>>> idx2_aid = invindex.idx2_daid
>>> daid2_label = invindex.daid2_label
>>> _ = helper_idf_wordgroup(wx2_idxs, idx2_aid, wx_series)
>>> idxs_list, aids_list = _
>>> wx2_idf = compute_idf_label1(wx_series, wx2_idxs, idx2_aid, daids)
"""
nWords = len(aids_list)
# Computes our novel label idf weight
lblindex_list = np.array(ut.tuples_to_unique_scalars(daid2_label.values()))
#daid2_lblindex = dict(zip(daid_list, lblindex_list))
unique_lblindexes, groupxs = clustertool.group_indices(lblindex_list)
daid_list = np.array(daid2_label.keys())
daids_list = [daid_list.take(xs) for xs in groupxs]
daid2_wxs = ut.ddict(list)
for wx, daids in enumerate(aids_list):
for daid in daids:
daid2_wxs[daid].append(wx)
lblindex2_daids = list(zip(unique_lblindexes, daids_list))
nLabels = len(unique_lblindexes)
pcntLblsWithWord = np.zeros(nWords, np.float64)
# Get num times word appears for eachlabel
for lblindex, daids in lblindex2_daids:
nWordsWithLabel = np.zeros(nWords)
for daid in daids:
wxs = daid2_wxs[daid]
nWordsWithLabel[wxs] += 1
pcntLblsWithWord += (1 - nWordsWithLabel.astype(np.float64) / len(daids))
# Labels for each word
idf_list = np.log(np.divide(nLabels, np.add(pcntLblsWithWord, 1),
dtype=hstypes.FLOAT_TYPE),
dtype=hstypes.FLOAT_TYPE)
return idf_list
def compute_idf_label1(aids_list, daid2_label):
"""
One of our idf extensions
Example:
>>> from ibeis.algo.hots.smk.smk_index import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, invindex, wx2_idxs, qparams = smk_debug.testdata_raw_internals1()
>>> wx_series = np.arange(len(invindex.words))
>>> idx2_aid = invindex.idx2_daid
>>> daid2_label = invindex.daid2_label
>>> _ = helper_idf_wordgroup(wx2_idxs, idx2_aid, wx_series)
>>> idxs_list, aids_list = _
>>> wx2_idf = compute_idf_label1(wx_series, wx2_idxs, idx2_aid, daids)
"""
nWords = len(aids_list)
# Computes our novel label idf weight
lblindex_list = np.array(ut.tuples_to_unique_scalars(daid2_label.values()))
#daid2_lblindex = dict(zip(daid_list, lblindex_list))
unique_lblindexes, groupxs = clustertool.group_indices(lblindex_list)
daid_list = np.array(daid2_label.keys())
daids_list = [daid_list.take(xs) for xs in groupxs]
daid2_wxs = ut.ddict(list)
for wx, daids in enumerate(aids_list):
for daid in daids:
daid2_wxs[daid].append(wx)
lblindex2_daids = list(zip(unique_lblindexes, daids_list))
nLabels = len(unique_lblindexes)
pcntLblsWithWord = np.zeros(nWords, np.float64)
# Get num times word appears for eachlabel
for lblindex, daids in lblindex2_daids:
nWordsWithLabel = np.zeros(nWords)
for daid in daids:
wxs = daid2_wxs[daid]
nWordsWithLabel[wxs] += 1
pcntLblsWithWord += (1 - nWordsWithLabel.astype(np.float64) / len(daids))
# Labels for each word
idf_list = np.log(np.divide(nLabels, np.add(pcntLblsWithWord, 1),
dtype=hstypes.FLOAT_TYPE),
dtype=hstypes.FLOAT_TYPE)
return idf_list
def build_daid2_chipmatch2(invindex, common_wxs, wx2_qaids, wx2_qfxs,
scores_list, daids_list, query_sccw):
"""
Builds explicit chipmatches that the rest of the pipeline plays nice with
Notation:
An explicit cmtup_old is a tuple (fm, fs, fk) feature_matches,
feature_scores, and feature_ranks.
Let N be the number of matches
A feature match, fm{shape=(N, 2), dtype=int32}, is an array where the first
column corresponds to query_feature_indexes (qfx) and the second column
corresponds to database_feature_indexes (dfx).
A feature score, fs{shape=(N,), dtype=float64} is an array of scores
A feature rank, fk{shape=(N,), dtype=int16} is an array of ranks
Returns:
daid2_chipmatch (dict) : (daid2_fm, daid2_fs, daid2_fk)
Return Format::
daid2_fm (dict): {daid: fm, ...}
daid2_fs (dict): {daid: fs, ...}
daid2_fk (dict): {daid: fk, ...}
Example:
>>> from ibeis.algo.hots.smk.smk_core import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, invindex, qindex, qparams = smk_debug.testdata_match_kernel_L2()
>>> wx2_qrvecs, wx2_qmaws, wx2_qaids, wx2_qfxs, query_sccw = qindex
>>> smk_alpha = ibs.cfg.query_cfg.smk_cfg.smk_alpha
>>> smk_thresh = ibs.cfg.query_cfg.smk_cfg.smk_thresh
>>> withinfo = True # takes an 11s vs 2s
>>> args = (wx2_qrvecs, wx2_qmaws, wx2_qaids, wx2_qfxs, query_sccw, invindex, withinfo, smk_alpha, smk_thresh)
>>> retL1 = match_kernel_L1(*args)
>>> (daid2_totalscore, common_wxs, scores_list, daids_list, idf_list, daid_agg_keys,) = retL1
>>> daid2_chipmatch_old = build_daid2_chipmatch2(invindex, common_wxs, wx2_qaids, wx2_qfxs, scores_list, daids_list, query_sccw)
>>> daid2_chipmatch_new = build_daid2_chipmatch3(invindex, common_wxs, wx2_qaids, wx2_qfxs, scores_list, daids_list, query_sccw)
>>> print(utool.is_dicteq(daid2_chipmatch_old[0], daid2_chipmatch_new[0]))
>>> print(utool.is_dicteq(daid2_chipmatch_old[2], daid2_chipmatch_new[2]))
>>> print(utool.is_dicteq(daid2_chipmatch_old[1], daid2_chipmatch_new[1]))
%timeit build_daid2_chipmatch2(invindex, common_wxs, wx2_qaids, wx2_qfxs, scores_list, daids_list, query_sccw)
%timeit build_daid2_chipmatch3(invindex, common_wxs, wx2_qaids, wx2_qfxs, scores_list, daids_list, query_sccw)
"""
# FIXME: move groupby to vtool
if utool.VERBOSE:
print('[smk_core] build cmtup_old')
wx2_dfxs = invindex.wx2_fxs
daid2_sccw = invindex.daid2_sccw
qfxs_list = [wx2_qfxs[wx] for wx in common_wxs]
dfxs_list = [wx2_dfxs[wx] for wx in common_wxs]
shapes_list = [scores.shape for scores in scores_list] # 51us
shape_ranges = [(mem_arange(w), mem_arange(h))
for (w, h) in shapes_list] # 230us
ijs_list = [
mem_meshgrid(wrange, hrange) for (wrange, hrange) in shape_ranges
] # 278us
# Normalize scores for words, nMatches, and query sccw (still need daid sccw)
nscores_iter = (scores * query_sccw for scores in scores_list)
# FIXME: Preflatten all of these lists
out_ijs = [list(zip(_is.flat, _js.flat)) for (_is, _js) in ijs_list]
out_qfxs = [[qfxs[ix] for (ix, jx) in ijs]
for (qfxs, ijs) in zip(qfxs_list, out_ijs)]
out_dfxs = [[dfxs[jx] for (ix, jx) in ijs]
for (dfxs, ijs) in zip(dfxs_list, out_ijs)]
out_daids = ([daids[jx] for (ix, jx) in ijs]
for (daids, ijs) in zip(daids_list, out_ijs))
out_scores = ([nscores[ijx] for ijx in ijs]
for (nscores, ijs) in zip(nscores_iter, out_ijs))
nested_fm_iter = [[
tuple(product(qfxs_, dfxs_)) for qfxs_, dfxs_ in zip(qfxs, dfxs)
] for qfxs, dfxs in zip(out_qfxs, out_dfxs)]
all_fms = np.array(list(utool.iflatten(utool.iflatten(nested_fm_iter))),
dtype=hstypes.FM_DTYPE)
nested_nmatch_list = [[len(fm) for fm in fms] for fms in nested_fm_iter]
nested_daid_iter = ([
[daid] * nMatch for nMatch, daid in zip(nMatch_list, daids)
] for nMatch_list, daids in zip(nested_nmatch_list, out_daids))
nested_score_iter = ([
[score / nMatch] * nMatch
for nMatch, score in zip(nMatch_list, scores)
] for nMatch_list, scores in zip(nested_nmatch_list, out_scores))
all_daids_ = np.array(list(utool.iflatten(
utool.iflatten(nested_daid_iter))),
dtype=hstypes.INDEX_TYPE)
all_fss = np.array(list(utool.iflatten(utool.iflatten(nested_score_iter))),
dtype=hstypes.FS_DTYPE)
# Filter out 0 scores
keep_xs = np.where(all_fss > 0)[0]
all_fss = all_fss.take(keep_xs)
all_fms = all_fms.take(keep_xs, axis=0)
all_daids_ = all_daids_.take(keep_xs)
daid_keys, groupxs = clustertool.group_indices(all_daids_)
fs_list = clustertool.apply_grouping(all_fss, groupxs)
fm_list = clustertool.apply_grouping(all_fms, groupxs)
daid2_fm = {daid: fm for daid, fm in zip(daid_keys, fm_list)}
daid2_fs = {
daid: fs * daid2_sccw[daid]
for daid, fs in zip(daid_keys, fs_list)
}
# FIXME: generalize to when nAssign > 1
daid2_fk = {
daid: np.ones(fs.size, dtype=hstypes.FK_DTYPE)
for daid, fs in zip(daid_keys, fs_list)
}
daid2_chipmatch = (daid2_fm, daid2_fs, daid2_fk)
return daid2_chipmatch
def build_daid2_chipmatch2(invindex, common_wxs, wx2_qaids, wx2_qfxs,
scores_list, daids_list, query_sccw):
"""
Builds explicit chipmatches that the rest of the pipeline plays nice with
Notation:
An explicit cmtup_old is a tuple (fm, fs, fk) feature_matches,
feature_scores, and feature_ranks.
Let N be the number of matches
A feature match, fm{shape=(N, 2), dtype=int32}, is an array where the first
column corresponds to query_feature_indexes (qfx) and the second column
corresponds to database_feature_indexes (dfx).
A feature score, fs{shape=(N,), dtype=float64} is an array of scores
A feature rank, fk{shape=(N,), dtype=int16} is an array of ranks
Returns:
daid2_chipmatch (dict) : (daid2_fm, daid2_fs, daid2_fk)
Return Format::
daid2_fm (dict): {daid: fm, ...}
daid2_fs (dict): {daid: fs, ...}
daid2_fk (dict): {daid: fk, ...}
Example:
>>> from ibeis.algo.hots.smk.smk_core import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, invindex, qindex, qparams = smk_debug.testdata_match_kernel_L2()
>>> wx2_qrvecs, wx2_qmaws, wx2_qaids, wx2_qfxs, query_sccw = qindex
>>> smk_alpha = ibs.cfg.query_cfg.smk_cfg.smk_alpha
>>> smk_thresh = ibs.cfg.query_cfg.smk_cfg.smk_thresh
>>> withinfo = True # takes an 11s vs 2s
>>> args = (wx2_qrvecs, wx2_qmaws, wx2_qaids, wx2_qfxs, query_sccw, invindex, withinfo, smk_alpha, smk_thresh)
>>> retL1 = match_kernel_L1(*args)
>>> (daid2_totalscore, common_wxs, scores_list, daids_list, idf_list, daid_agg_keys,) = retL1
>>> daid2_chipmatch_old = build_daid2_chipmatch2(invindex, common_wxs, wx2_qaids, wx2_qfxs, scores_list, daids_list, query_sccw)
>>> daid2_chipmatch_new = build_daid2_chipmatch3(invindex, common_wxs, wx2_qaids, wx2_qfxs, scores_list, daids_list, query_sccw)
>>> print(utool.is_dicteq(daid2_chipmatch_old[0], daid2_chipmatch_new[0]))
>>> print(utool.is_dicteq(daid2_chipmatch_old[2], daid2_chipmatch_new[2]))
>>> print(utool.is_dicteq(daid2_chipmatch_old[1], daid2_chipmatch_new[1]))
%timeit build_daid2_chipmatch2(invindex, common_wxs, wx2_qaids, wx2_qfxs, scores_list, daids_list, query_sccw)
%timeit build_daid2_chipmatch3(invindex, common_wxs, wx2_qaids, wx2_qfxs, scores_list, daids_list, query_sccw)
"""
# FIXME: move groupby to vtool
if utool.VERBOSE:
print('[smk_core] build cmtup_old')
wx2_dfxs = invindex.wx2_fxs
daid2_sccw = invindex.daid2_sccw
qfxs_list = [wx2_qfxs[wx] for wx in common_wxs]
dfxs_list = [wx2_dfxs[wx] for wx in common_wxs]
shapes_list = [scores.shape for scores in scores_list] # 51us
shape_ranges = [(mem_arange(w), mem_arange(h)) for (w, h) in shapes_list] # 230us
ijs_list = [mem_meshgrid(wrange, hrange) for (wrange, hrange) in shape_ranges] # 278us
# Normalize scores for words, nMatches, and query sccw (still need daid sccw)
nscores_iter = (scores * query_sccw for scores in scores_list)
# FIXME: Preflatten all of these lists
out_ijs = [
list(zip(_is.flat, _js.flat))
for (_is, _js) in ijs_list
]
out_qfxs = [
[qfxs[ix] for (ix, jx) in ijs]
for (qfxs, ijs) in zip(qfxs_list, out_ijs)
]
out_dfxs = [
[dfxs[jx] for (ix, jx) in ijs]
for (dfxs, ijs) in zip(dfxs_list, out_ijs)
]
out_daids = (
[daids[jx] for (ix, jx) in ijs]
for (daids, ijs) in zip(daids_list, out_ijs)
)
out_scores = (
[nscores[ijx] for ijx in ijs]
for (nscores, ijs) in zip(nscores_iter, out_ijs)
)
nested_fm_iter = [
[
tuple(product(qfxs_, dfxs_))
for qfxs_, dfxs_ in zip(qfxs, dfxs)
]
for qfxs, dfxs in zip(out_qfxs, out_dfxs)
]
all_fms = np.array(list(utool.iflatten(utool.iflatten(nested_fm_iter))), dtype=hstypes.FM_DTYPE)
nested_nmatch_list = [[len(fm) for fm in fms] for fms in nested_fm_iter]
nested_daid_iter = (
[
[daid] * nMatch
for nMatch, daid in zip(nMatch_list, daids)
]
for nMatch_list, daids in zip(nested_nmatch_list, out_daids)
)
nested_score_iter = (
[
[score / nMatch] * nMatch
for nMatch, score in zip(nMatch_list, scores)
]
for nMatch_list, scores in zip(nested_nmatch_list, out_scores)
)
all_daids_ = np.array(list(utool.iflatten(utool.iflatten(nested_daid_iter))), dtype=hstypes.INDEX_TYPE)
all_fss = np.array(list(utool.iflatten(utool.iflatten(nested_score_iter))), dtype=hstypes.FS_DTYPE)
# Filter out 0 scores
keep_xs = np.where(all_fss > 0)[0]
all_fss = all_fss.take(keep_xs)
all_fms = all_fms.take(keep_xs, axis=0)
all_daids_ = all_daids_.take(keep_xs)
daid_keys, groupxs = clustertool.group_indices(all_daids_)
fs_list = clustertool.apply_grouping(all_fss, groupxs)
fm_list = clustertool.apply_grouping(all_fms, groupxs)
daid2_fm = {daid: fm for daid, fm in zip(daid_keys, fm_list)}
daid2_fs = {daid: fs * daid2_sccw[daid] for daid, fs in zip(daid_keys, fs_list)}
# FIXME: generalize to when nAssign > 1
daid2_fk = {daid: np.ones(fs.size, dtype=hstypes.FK_DTYPE) for daid, fs in zip(daid_keys, fs_list)}
daid2_chipmatch = (daid2_fm, daid2_fs, daid2_fk)
return daid2_chipmatch
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_negentropy_names(aids_list, daid2_label):
r"""
One of our idf extensions
Word weighting based on the negative entropy over all names of p(n_i | word)
Args:
aids_list (list of aids):
daid2_label (dict from daid to label):
Returns:
negentropy_list (ndarray[float32]): idf-like weighting for each word based on the negative entropy
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.hots.smk.smk_index import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, invindex, wx2_idxs, qparams = smk_debug.testdata_raw_internals1()
>>> wx_series = np.arange(len(invindex.words))
>>> idx2_aid = invindex.idx2_daid
>>> daid2_label = invindex.daid2_label
>>> _ = helper_idf_wordgroup(wx2_idxs, idx2_aid, wx_series)
>>> idxs_list, aids_list = _
Math::
p(n_i | \word) = \sum_{\lbl \in L_i} p(\lbl | \word)
p(\lbl | \word) = \frac{p(\word | \lbl) p(\lbl)}{p(\word)}
p(\word) = \sum_{\lbl' \in L} p(\word | \lbl') p(\lbl')
p(\word | \lbl) = NumAnnotOfLabelWithWord / NumAnnotWithLabel =
\frac{\sum_{\X \in \DB_\lbl} b(\word, \X)}{\card{\DB_\lbl}}
h(n_i | word) = -\sum_{i=1}^N p(n_i | \word) \log p(n_i | \word)
word_weight = log(N) - h(n | word)
CommandLine:
python dev.py -t smk2 --allgt --db GZ_ALL
python dev.py -t smk5 --allgt --db GZ_ALL
Auto:
python -c "import utool as ut; ut.print_auto_docstr('ibeis.algo.hots.smk.smk_index', 'compute_negentropy_names')"
"""
nWords = len(aids_list)
# --- LABEL MEMBERS w.r.t daids ---
# compute mapping from label to daids
# Translate tuples into scalars for efficiency
label_list = list(daid2_label.values())
lblindex_list = np.array(ut.tuples_to_unique_scalars(label_list))
#daid2_lblindex = dict(zip(daid_list, lblindex_list))
unique_lblindexes, groupxs = clustertool.group_indices(lblindex_list)
daid_list = np.array(daid2_label.keys())
daids_list = [daid_list.take(xs) for xs in groupxs]
# --- DAID MEMBERS w.r.t. words ---
# compute mapping from daid to word indexes
# finds all the words that belong to an annotation
daid2_wxs = ut.ddict(list)
for wx, _daids in enumerate(aids_list):
for daid in _daids:
daid2_wxs[daid].append(wx)
# --- \Pr(\word \given \lbl) for each label ---
# Compute the number of annotations in a label with the word vs
# the number of annotations in the label
lblindex2_daids = list(zip(unique_lblindexes, daids_list))
# Get num times word appears for each label
probWordGivenLabel_list = []
for lblindex, _daids in lblindex2_daids:
nAnnotOfLabelWithWord = np.zeros(nWords, dtype=np.int32)
for daid in _daids:
wxs = np.unique(daid2_wxs[daid])
nAnnotOfLabelWithWord[wxs] += 1
probWordGivenLabel = nAnnotOfLabelWithWord.astype(np.float64) / len(_daids)
probWordGivenLabel_list.append(probWordGivenLabel)
# (nLabels, nWords)
probWordGivenLabel_arr = np.array(probWordGivenLabel_list)
# --- \Pr(\lbl \given \word) ---
# compute partition function that approximates probability of a word
# (1, nWords)
probWord = probWordGivenLabel_arr.sum(axis=0)
probWord.shape = (1, probWord.size)
# (nLabels, nWords)
probLabelGivenWord_arr = (probWordGivenLabel_arr / probWord)
# --- \Pr(\name \given \lbl) ---
# get names for each unique label
nid_list = np.array([label_list[xs[0]][0] for xs in groupxs])
unique_nids, groupxs_ = clustertool.group_indices(nid_list)
# (nNames, nWords)
# add a little wiggle room
eps = 1E-9
# http://stackoverflow.com/questions/872544/precision-of-floating-point
#epsilon = 2^(E-52) % For a 64-bit float (double precision)
#epsilon = 2^(E-23) % For a 32-bit float (single precision)
#epsilon = 2^(E-10) % For a 16-bit float (half precision)
probNameGivenWord = eps + (1.0 - eps) * np.array([probLabelGivenWord_arr.take(xs, axis=0).sum(axis=0) for xs in groupxs_])
logProbNameGivenWord = np.log(probNameGivenWord)
wordNameEntropy = -(probNameGivenWord * logProbNameGivenWord).sum(0)
# Compute negative entropy for weights
nNames = len(nid_list)
negentropy_list = np.log(nNames) - wordNameEntropy
return negentropy_list
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
def compute_negentropy_names(aids_list, daid2_label):
r"""
One of our idf extensions
Word weighting based on the negative entropy over all names of p(n_i | word)
Args:
aids_list (list of aids):
daid2_label (dict from daid to label):
Returns:
negentropy_list (ndarray[float32]): idf-like weighting for each word based on the negative entropy
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.hots.smk.smk_index import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, invindex, wx2_idxs, qparams = smk_debug.testdata_raw_internals1()
>>> wx_series = np.arange(len(invindex.words))
>>> idx2_aid = invindex.idx2_daid
>>> daid2_label = invindex.daid2_label
>>> _ = helper_idf_wordgroup(wx2_idxs, idx2_aid, wx_series)
>>> idxs_list, aids_list = _
Math::
p(n_i | \word) = \sum_{\lbl \in L_i} p(\lbl | \word)
p(\lbl | \word) = \frac{p(\word | \lbl) p(\lbl)}{p(\word)}
p(\word) = \sum_{\lbl' \in L} p(\word | \lbl') p(\lbl')
p(\word | \lbl) = NumAnnotOfLabelWithWord / NumAnnotWithLabel =
\frac{\sum_{\X \in \DB_\lbl} b(\word, \X)}{\card{\DB_\lbl}}
h(n_i | word) = -\sum_{i=1}^N p(n_i | \word) \log p(n_i | \word)
word_weight = log(N) - h(n | word)
CommandLine:
python dev.py -t smk2 --allgt --db GZ_ALL
python dev.py -t smk5 --allgt --db GZ_ALL
Auto:
python -c "import utool as ut; ut.print_auto_docstr('ibeis.algo.hots.smk.smk_index', 'compute_negentropy_names')"
"""
nWords = len(aids_list)
# --- LABEL MEMBERS w.r.t daids ---
# compute mapping from label to daids
# Translate tuples into scalars for efficiency
label_list = list(daid2_label.values())
lblindex_list = np.array(ut.tuples_to_unique_scalars(label_list))
#daid2_lblindex = dict(zip(daid_list, lblindex_list))
unique_lblindexes, groupxs = clustertool.group_indices(lblindex_list)
daid_list = np.array(daid2_label.keys())
daids_list = [daid_list.take(xs) for xs in groupxs]
# --- DAID MEMBERS w.r.t. words ---
# compute mapping from daid to word indexes
# finds all the words that belong to an annotation
daid2_wxs = ut.ddict(list)
for wx, _daids in enumerate(aids_list):
for daid in _daids:
daid2_wxs[daid].append(wx)
# --- \Pr(\word \given \lbl) for each label ---
# Compute the number of annotations in a label with the word vs
# the number of annotations in the label
lblindex2_daids = list(zip(unique_lblindexes, daids_list))
# Get num times word appears for each label
probWordGivenLabel_list = []
for lblindex, _daids in lblindex2_daids:
nAnnotOfLabelWithWord = np.zeros(nWords, dtype=np.int32)
for daid in _daids:
wxs = np.unique(daid2_wxs[daid])
nAnnotOfLabelWithWord[wxs] += 1
probWordGivenLabel = nAnnotOfLabelWithWord.astype(np.float64) / len(_daids)
probWordGivenLabel_list.append(probWordGivenLabel)
# (nLabels, nWords)
probWordGivenLabel_arr = np.array(probWordGivenLabel_list)
# --- \Pr(\lbl \given \word) ---
# compute partition function that approximates probability of a word
# (1, nWords)
probWord = probWordGivenLabel_arr.sum(axis=0)
probWord.shape = (1, probWord.size)
# (nLabels, nWords)
probLabelGivenWord_arr = (probWordGivenLabel_arr / probWord)
# --- \Pr(\name \given \lbl) ---
# get names for each unique label
nid_list = np.array([label_list[xs[0]][0] for xs in groupxs])
unique_nids, groupxs_ = clustertool.group_indices(nid_list)
# (nNames, nWords)
# add a little wiggle room
eps = 1E-9
# http://stackoverflow.com/questions/872544/precision-of-floating-point
#epsilon = 2^(E-52) # For a 64-bit float (double precision)
#epsilon = 2^(E-23) # For a 32-bit float (single precision)
#epsilon = 2^(E-10) # For a 16-bit float (half precision)
probNameGivenWord = eps + (1.0 - eps) * np.array([probLabelGivenWord_arr.take(xs, axis=0).sum(axis=0) for xs in groupxs_])
logProbNameGivenWord = np.log(probNameGivenWord)
wordNameEntropy = -(probNameGivenWord * logProbNameGivenWord).sum(0)
# Compute negative entropy for weights
nNames = len(nid_list)
negentropy_list = np.log(nNames) - wordNameEntropy
return negentropy_list
