def compute_residuals_(words, wx2_idxs, idx2_vec, idx2_aid, idx2_fx, aggregate, with_pandas=WITH_PANDAS): """ Computes residual vectors based on word assignments returns mapping from word index to a set of residual vectors Output: wx2_rvecs - [ ... [ rvec_i1, ..., rvec_Mi ]_i ... ] wx2_aids - [ ... [ aid_i1, ..., aid_Mi ]_i ... ] wx2_fxs - [ ... [[fxs]_i1, ..., [fxs]_Mi ]_i ... ] For every word: * list of aggvecs For every aggvec: * one parent aid, if aggregate is False: assert isunique(aids) * list of parent fxs, if aggregate is True: assert len(fxs) == 1 >>> from ibeis.model.hots.smk.smk_index import * # NOQA >>> from ibeis.model.hots.smk import smk_debug >>> ibs, annots_df, daids, qaids, invindex, wx2_idxs = smk_debug.testdata_raw_internals1() >>> words = invindex.words >>> idx2_aid = invindex.idx2_daid >>> idx2_fx = invindex.idx2_dfx >>> idx2_vec = invindex.idx2_dvec >>> aggregate = ibs.cfg.query_cfg.smk_cfg.aggregate >>> wx2_rvecs, wx2_aids = compute_residuals_(words, wx2_idxs, idx2_vec, idx2_aid, idx2_fx, aggregate) """ words_values = pdh.ensure_values(words) idx2_aid_values = pdh.ensure_values(idx2_aid) idx2_vec_values = pdh.ensure_values(idx2_vec) idx2_fx_values = pdh.ensure_values(idx2_fx) wx_sublist = pdh.ensure_index(wx2_idxs) # Build lists w.r.t. words idxs_list = pdh.ensure_values_subset(wx2_idxs, wx_sublist) aids_list = [idx2_aid_values.take(idxs) for idxs in idxs_list] #wx2_idxs_values = pdh.ensure_values_subset(wx2_idxs, wx_sublist) #idxs_list = [pdh.ensure_values(idxsdf).astype(INDEX_TYPE) for idxsdf in wx2_idxs_values] # 13 ms if utool.DEBUG2: #assert np.all(np.diff(wx_sublist) == 1), 'not dense' assert all([len(a) == len(b) for a, b in zip(idxs_list, aids_list)]), 'bad alignment' assert idx2_vec_values.shape[0] == idx2_fx_values.shape[0] assert idx2_vec_values.shape[0] == idx2_aid_values.shape[0] # Prealloc output if utool.VERBOSE: print('[smk_index] Residual Vectors for %d words. aggregate=%r' % (len(wx2_idxs), aggregate,)) # Nonaggregated residuals #_args1 = (words_values, wx_sublist, idxs_list, idx2_vec_values) #rvecs_list = smk_speed.compute_nonagg_rvec_listcomp(*_args1) # 125 ms 11% words_list = [words_values[wx:wx + 1] for wx in wx_sublist] # 1 ms vecs_list = [idx2_vec_values.take(idxs, axis=0) for idxs in idxs_list] # 5.3 ms rvecs_list = [smk_core.get_norm_rvecs(vecs, word) for vecs, word in zip(vecs_list, words_list)] # 103 ms # 90% if aggregate: # Aggregate over words of the same aid tup = smk_speed.compute_agg_rvecs(rvecs_list, idxs_list, aids_list) # 38% (aggvecs_list, aggaids_list, aggidxs_list) = tup aggfxs_list = [[idx2_fx_values.take(idxs) for idxs in aggidxs] for aggidxs in aggidxs_list] if with_pandas: _args2 = (wx_sublist, aggvecs_list, aggaids_list, aggfxs_list) # Make aggregate dataframes wx2_aggvecs, wx2_aggaids, wx2_aggfxs = pdh.pandasify_agg_list(*_args2) # 617 ms 47% else: wx2_aggvecs = {wx: aggvecs for wx, aggvecs in zip(wx_sublist, aggvecs_list)} wx2_aggaids = {wx: aggaids for wx, aggaids in zip(wx_sublist, aggaids_list)} wx2_aggfxs = {wx: aggfxs for wx, aggfxs in zip(wx_sublist, aggfxs_list)} if utool.DEBUG2: from ibeis.model.hots.smk import smk_debug smk_debug.check_wx2(words, wx2_aggvecs, wx2_aggaids, wx2_aggfxs) return wx2_aggvecs, wx2_aggaids, wx2_aggfxs else: # Make residuals dataframes # compatibility hack fxs_list = [[idx2_fx_values[idx:idx + 1] for idx in idxs] for idxs in idxs_list] if with_pandas: _args3 = (wx_sublist, idxs_list, rvecs_list, aids_list, fxs_list) wx2_rvecs, wx2_aids, wx2_fxs = pdh.pandasify_rvecs_list(*_args3) # 405 ms else: wx2_rvecs = {wx: rvecs for wx, rvecs in zip(wx_sublist, rvecs_list)} wx2_aids = {wx: aids for wx, aids in zip(wx_sublist, aids_list)} wx2_fxs = {wx: fxs for wx, fxs in zip(wx_sublist, fxs_list)} if utool.DEBUG2: from ibeis.model.hots.smk import smk_debug smk_debug.check_wx2(words, wx2_rvecs, wx2_aids, wx2_fxs) return wx2_rvecs, wx2_aids, wx2_fxs
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_residuals_(words, wx2_idxs, idx2_vec, idx2_aid, idx2_fx, aggregate, with_pandas=WITH_PANDAS): """ Computes residual vectors based on word assignments returns mapping from word index to a set of residual vectors Output: wx2_rvecs - [ ... [ rvec_i1, ..., rvec_Mi ]_i ... ] wx2_aids - [ ... [ aid_i1, ..., aid_Mi ]_i ... ] wx2_fxs - [ ... [[fxs]_i1, ..., [fxs]_Mi ]_i ... ] For every word: * list of aggvecs For every aggvec: * one parent aid, if aggregate is False: assert isunique(aids) * list of parent fxs, if aggregate is True: assert len(fxs) == 1 >>> from ibeis.model.hots.smk.smk_index import * # NOQA >>> from ibeis.model.hots.smk import smk_debug >>> ibs, annots_df, daids, qaids, invindex, wx2_idxs = smk_debug.testdata_raw_internals1() >>> words = invindex.words >>> idx2_aid = invindex.idx2_daid >>> idx2_fx = invindex.idx2_dfx >>> idx2_vec = invindex.idx2_dvec >>> aggregate = ibs.cfg.query_cfg.smk_cfg.aggregate >>> wx2_rvecs, wx2_aids = compute_residuals_(words, wx2_idxs, idx2_vec, idx2_aid, idx2_fx, aggregate) """ words_values = pdh.ensure_values(words) idx2_aid_values = pdh.ensure_values(idx2_aid) idx2_vec_values = pdh.ensure_values(idx2_vec) idx2_fx_values = pdh.ensure_values(idx2_fx) wx_sublist = pdh.ensure_index(wx2_idxs) # Build lists w.r.t. words idxs_list = pdh.ensure_values_subset(wx2_idxs, wx_sublist) aids_list = [idx2_aid_values.take(idxs) for idxs in idxs_list] #wx2_idxs_values = pdh.ensure_values_subset(wx2_idxs, wx_sublist) #idxs_list = [pdh.ensure_values(idxsdf).astype(INDEX_TYPE) for idxsdf in wx2_idxs_values] # 13 ms if utool.DEBUG2: #assert np.all(np.diff(wx_sublist) == 1), 'not dense' assert all([len(a) == len(b) for a, b in zip(idxs_list, aids_list)]), 'bad alignment' assert idx2_vec_values.shape[0] == idx2_fx_values.shape[0] assert idx2_vec_values.shape[0] == idx2_aid_values.shape[0] # Prealloc output if utool.VERBOSE: print('[smk_index] Residual Vectors for %d words. aggregate=%r' % ( len(wx2_idxs), aggregate, )) # Nonaggregated residuals #_args1 = (words_values, wx_sublist, idxs_list, idx2_vec_values) #rvecs_list = smk_speed.compute_nonagg_rvec_listcomp(*_args1) # 125 ms 11% words_list = [words_values[wx:wx + 1] for wx in wx_sublist] # 1 ms vecs_list = [idx2_vec_values.take(idxs, axis=0) for idxs in idxs_list] # 5.3 ms rvecs_list = [ smk_core.get_norm_rvecs(vecs, word) for vecs, word in zip(vecs_list, words_list) ] # 103 ms # 90% if aggregate: # Aggregate over words of the same aid tup = smk_speed.compute_agg_rvecs(rvecs_list, idxs_list, aids_list) # 38% (aggvecs_list, aggaids_list, aggidxs_list) = tup aggfxs_list = [[idx2_fx_values.take(idxs) for idxs in aggidxs] for aggidxs in aggidxs_list] if with_pandas: _args2 = (wx_sublist, aggvecs_list, aggaids_list, aggfxs_list) # Make aggregate dataframes wx2_aggvecs, wx2_aggaids, wx2_aggfxs = pdh.pandasify_agg_list( *_args2) # 617 ms 47% else: wx2_aggvecs = { wx: aggvecs for wx, aggvecs in zip(wx_sublist, aggvecs_list) } wx2_aggaids = { wx: aggaids for wx, aggaids in zip(wx_sublist, aggaids_list) } wx2_aggfxs = { wx: aggfxs for wx, aggfxs in zip(wx_sublist, aggfxs_list) } if utool.DEBUG2: from ibeis.model.hots.smk import smk_debug smk_debug.check_wx2(words, wx2_aggvecs, wx2_aggaids, wx2_aggfxs) return wx2_aggvecs, wx2_aggaids, wx2_aggfxs else: # Make residuals dataframes # compatibility hack fxs_list = [[idx2_fx_values[idx:idx + 1] for idx in idxs] for idxs in idxs_list] if with_pandas: _args3 = (wx_sublist, idxs_list, rvecs_list, aids_list, fxs_list) wx2_rvecs, wx2_aids, wx2_fxs = pdh.pandasify_rvecs_list( *_args3) # 405 ms else: wx2_rvecs = { wx: rvecs for wx, rvecs in zip(wx_sublist, rvecs_list) } wx2_aids = {wx: aids for wx, aids in zip(wx_sublist, aids_list)} wx2_fxs = {wx: fxs for wx, fxs in zip(wx_sublist, fxs_list)} if utool.DEBUG2: from ibeis.model.hots.smk import smk_debug smk_debug.check_wx2(words, wx2_rvecs, wx2_aids, wx2_fxs) return wx2_rvecs, wx2_aids, wx2_fxs
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