def get_varied_pipecfg_lbls(cfgdict_list, pipecfg_list=None):
if pipecfg_list is None:
from ibeis.algo import Config
cfg_default_dict = dict(Config.QueryConfig().parse_items())
cfgx2_lbl = ut.get_varied_cfg_lbls(cfgdict_list, cfg_default_dict)
else:
# TODO: group cfgdict by config type and then get varied labels
cfg_default_dict = None
cfgx2_lbl = ut.get_varied_cfg_lbls(cfgdict_list, cfg_default_dict)
return cfgx2_lbl
def get_varied_pipecfg_lbls(cfgdict_list, pipecfg_list=None):
if pipecfg_list is None:
from ibeis.algo import Config
#cls_list = [Config] * len(cfgdict_list)
cfg_default_dict = dict(Config.QueryConfig().parse_items())
cfgx2_lbl = ut.get_varied_cfg_lbls(cfgdict_list, cfg_default_dict)
else:
# TODO: group cfgdict by config type and then get varied labels
cfg_default_dict = None
cfgx2_lbl = ut.get_varied_cfg_lbls(cfgdict_list, cfg_default_dict)
return cfgx2_lbl
def compare_featscores():
"""
CommandLine:
ibeis --tf compare_featscores --db PZ_MTEST \
--nfscfg :disttype=[L2_sift,lnbnn],top_percent=[None,.5,.1] -a timectrl \
-p default:K=[1,2],normalizer_rule=name \
--save featscore{db}.png --figsize=13,20 --diskshow
ibeis --tf compare_featscores --db PZ_MTEST \
--nfscfg :disttype=[L2_sift,normdist,lnbnn],top_percent=[None,.5] -a timectrl \
-p default:K=[1],normalizer_rule=name,sv_on=[True,False] \
--save featscore{db}.png --figsize=13,10 --diskshow
ibeis --tf compare_featscores --nfscfg :disttype=[L2_sift,normdist,lnbnn] \
-a timectrl -p default:K=1,normalizer_rule=name --db PZ_Master1 \
--save featscore{db}.png --figsize=13,13 --diskshow
ibeis --tf compare_featscores --nfscfg :disttype=[L2_sift,normdist,lnbnn] \
-a timectrl -p default:K=1,normalizer_rule=name --db GZ_ALL \
--save featscore{db}.png --figsize=13,13 --diskshow
ibeis --tf compare_featscores --db GIRM_Master1 \
--nfscfg ':disttype=fg,L2_sift,normdist,lnbnn' \
-a timectrl -p default:K=1,normalizer_rule=name \
--save featscore{db}.png --figsize=13,13
ibeis --tf compare_featscores --nfscfg :disttype=[L2_sift,normdist,lnbnn] \
-a timectrl -p default:K=[1,2,3],normalizer_rule=name,sv_on=False \
--db PZ_Master1 --save featscore{db}.png \
--dpi=128 --figsize=15,20 --diskshow
ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db PZ_MTEST
ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db GZ_ALL
ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db PZ_Master1
ibeis --tf compare_featscores --show --nfscfg :disttype=[L2_sift,normdist] -a timectrl -p :K=1 --db GIRM_Master1
ibeis --tf compare_featscores --db PZ_MTEST \
--nfscfg :disttype=[L2_sift,normdist,lnbnn],top_percent=[None,.5,.2] -a timectrl \
-p default:K=[1],normalizer_rule=name \
--save featscore{db}.png --figsize=13,20 --diskshow
ibeis --tf compare_featscores --db PZ_MTEST \
--nfscfg :disttype=[L2_sift,normdist,lnbnn],top_percent=[None,.5,.2] -a timectrl \
-p default:K=[1],normalizer_rule=name \
--save featscore{db}.png --figsize=13,20 --diskshow
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.scorenorm import * # NOQA
>>> result = compare_featscores()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
import ibeis
nfs_cfg_list = NormFeatScoreConfig.from_argv_cfgs()
learnkw = {}
ibs, testres = ibeis.testdata_expts(
defaultdb='PZ_MTEST', a=['default'], p=['default:K=1'])
print('nfs_cfg_list = ' + ut.repr3(nfs_cfg_list))
encoder_list = []
lbl_list = []
varied_nfs_lbls = ut.get_varied_cfg_lbls(nfs_cfg_list)
varied_qreq_lbls = ut.get_varied_cfg_lbls(testres.cfgdict_list)
#varies_qreq_lbls
#func = ut.cached_func(cache_dir='.')(learn_featscore_normalizer)
for datakw, nlbl in zip(nfs_cfg_list, varied_nfs_lbls):
for qreq_, qlbl in zip(testres.cfgx2_qreq_, varied_qreq_lbls):
lbl = qlbl + ' ' + nlbl
cfgstr = '_'.join([datakw.get_cfgstr(), qreq_.get_full_cfgstr()])
try:
encoder = vt.ScoreNormalizer()
encoder.load(cfgstr=cfgstr)
except IOError:
print('datakw = %r' % (datakw,))
encoder = learn_featscore_normalizer(qreq_, datakw, learnkw)
encoder.save(cfgstr=cfgstr)
encoder_list.append(encoder)
lbl_list.append(lbl)
fnum = 1
# next_pnum = pt.make_pnum_nextgen(nRows=len(encoder_list), nCols=3)
next_pnum = pt.make_pnum_nextgen(nRows=len(encoder_list) + 1, nCols=3, start=3)
iconsize = 94
if len(encoder_list) > 3:
iconsize = 64
icon = qreq_.ibs.get_database_icon(max_dsize=(None, iconsize), aid=qreq_.qaids[0])
score_range = (0, .6)
for encoder, lbl in zip(encoder_list, lbl_list):
#encoder.visualize(figtitle=encoder.get_cfgstr(), with_prebayes=False, with_postbayes=False)
encoder._plot_score_support_hist(fnum, pnum=next_pnum(), titlesuf='\n' + lbl, score_range=score_range)
encoder._plot_prebayes(fnum, pnum=next_pnum())
encoder._plot_roc(fnum, pnum=next_pnum())
if icon is not None:
pt.overlay_icon(icon, coords=(1, 0), bbox_alignment=(1, 0))
nonvaried_lbl = ut.get_nonvaried_cfg_lbls(nfs_cfg_list)[0]
figtitle = qreq_.__str__() + '\n' + nonvaried_lbl
pt.set_figtitle(figtitle)
pt.adjust_subplots(hspace=.5, top=.92, bottom=.08, left=.1, right=.9)
pt.update_figsize()
pt.plt.tight_layout()
