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()
def show_time_distributions(ibs, unixtime_list):
r"""
"""
#import vtool as vt
import plottool as pt
unixtime_list = np.array(unixtime_list)
num_nan = np.isnan(unixtime_list).sum()
num_total = len(unixtime_list)
unixtime_list = unixtime_list[~np.isnan(unixtime_list)]
if False:
from matplotlib import dates as mpldates
#data_list = list(map(ut.unixtime_to_datetimeobj, unixtime_list))
n, bins, patches = pt.plt.hist(unixtime_list, 365)
#n_ = list(map(ut.unixtime_to_datetimeobj, n))
#bins_ = list(map(ut.unixtime_to_datetimeobj, bins))
pt.plt.setp(patches, 'facecolor', 'g', 'alpha', 0.75)
ax = pt.gca()
#ax.xaxis.set_major_locator(mpldates.YearLocator())
#hfmt = mpldates.DateFormatter('%y/%m/%d')
#ax.xaxis.set_major_formatter(hfmt)
mpldates.num2date(unixtime_list)
#pt.gcf().autofmt_xdate()
#y = pt.plt.normpdf( bins, unixtime_list.mean(), unixtime_list.std())
#ax.set_xticks(bins_)
#l = pt.plt.plot(bins_, y, 'k--', linewidth=1.5)
else:
pt.draw_time_distribution(unixtime_list)
#pt.draw_histogram()
ax = pt.gca()
ax.set_xlabel('Date')
ax.set_title('Timestamp distribution of %s. #nan=%d/%d' % (
ibs.get_dbname_alias(),
num_nan, num_total))
pt.gcf().autofmt_xdate()
icon = ibs.get_database_icon()
if icon is not None:
#import matplotlib as mpl
#import vtool as vt
ax = pt.gca()
# Overlay a species icon
# http://matplotlib.org/examples/pylab_examples/demo_annotation_box.html
#icon = vt.convert_image_list_colorspace([icon], 'RGB', 'BGR')[0]
pt.overlay_icon(icon, coords=(0, 1), bbox_alignment=(0, 1))
#imagebox = mpl.offsetbox.OffsetImage(icon, zoom=1.0)
##xy = [ax.get_xlim()[0] + 5, ax.get_ylim()[1]]
##ax.set_xlim(1, 100)
##ax.set_ylim(0, 100)
##x = np.array(ax.get_xlim()).sum() / 2
##y = np.array(ax.get_ylim()).sum() / 2
##xy = [x, y]
##print('xy = %r' % (xy,))
##x = np.nanmin(unixtime_list)
##xy = [x, y]
##print('xy = %r' % (xy,))
##ax.get_ylim()[0]]
#xy = [ax.get_xlim()[0], ax.get_ylim()[1]]
#ab = mpl.offsetbox.AnnotationBbox(
# imagebox, xy, xycoords='data',
# xybox=(-0., 0.),
# boxcoords="offset points",
# box_alignment=(0, 1), pad=0.0)
#ax.add_artist(ab)
if ut.get_argflag('--contextadjust'):
#pt.adjust_subplots2(left=.08, bottom=.1, top=.9, wspace=.3, hspace=.1)
pt.adjust_subplots2(use_argv=True)
def sift_dataset_separability(dataset):
"""
VERY HACKED RIGHT NOW. ONLY LIBERTY. BLINDLY CACHES
Args:
dataset (?):
CommandLine:
python -m ibeis_cnn.experiments --exec-sift_dataset_separability --show
Example:
>>> # SCRIPT
>>> from ibeis_cnn.experiments import * # NOQA
>>> from ibeis_cnn import ingest_data
>>> dataset = ingest_data.grab_liberty_siam_dataset(250000)
>>> ut.quit_if_noshow()
>>> sift_dataset_separability(dataset)
>>> ut.show_if_requested()
"""
import vtool as vt
@ut.cached_func('tempsiftscorecache', cache_dir='.')
def cached_siftscores():
data, labels = dataset.subset('test')
sift_scores, sift_list = test_sift_patchmatch_scores(data, labels)
sift_scores = sift_scores.astype(np.float64)
return sift_scores, labels, sift_list
sift_scores, labels, sift_list = cached_siftscores()
# I dont think we can compare lnbnn on liberty
# because we dont have a set of id labels, we have
# pairs of correspondences.
#import pyflann
#flann = pyflann.FLANN()
#flann.build_index(sift_list)
#idxs, dists = flann.nn_index(sift_list, 10)
encoder_kw = {
#'monotonize': False,
'monotonize': True,
}
sift_encoder = vt.ScoreNormalizer(**encoder_kw)
sift_encoder.fit(sift_scores, labels)
dataname = dataset.alias_key
viz_kw = dict(
with_scores=False,
with_postbayes=False,
with_prebayes=False,
target_tpr=.95,
score_range=(0, 1)
)
inter_sift = sift_encoder.visualize(
figtitle=dataname + ' SIFT scores. #data=' + str(len(labels)),
fnum=None, **viz_kw)
import plottool as pt
#icon = ibs.get_database_icon()
icon = ('http://www.councilchronicle.com/wp-content/uploads/2015/08/'
'West-Virginia-Arrested-over-Bogus-Statue-of-Liberty-Bomb-Threat.jpg')
if icon is not None:
pt.overlay_icon(icon, coords=(1, 0), bbox_alignment=(1, 0), max_dsize=(None, 192))
if ut.get_argflag('--contextadjust'):
pt.adjust_subplots(left=.1, bottom=.25, wspace=.2, hspace=.2)
pt.adjust_subplots(use_argv=True)
return inter_sift
def draw_feat_scoresep(testres, f=None, disttype=None):
r"""
SeeAlso:
ibeis.algo.hots.scorenorm.train_featscore_normalizer
CommandLine:
python -m ibeis --tf TestResult.draw_feat_scoresep --show
python -m ibeis --tf TestResult.draw_feat_scoresep --show -t default:sv_on=[True,False]
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_Master1
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_Master1 --disttype=L2_sift,fg
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_Master1 --disttype=L2_sift
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST -t best:lnbnn_on=True --namemode=True
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST -t best:lnbnn_on=True --namemode=False
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST --disttype=L2_sift
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST --disttype=L2_sift -t best:SV=False
utprof.py -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_Master1
utprof.py -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_Master1 --fsvx=1:2
utprof.py -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_Master1 --fsvx=0:1
utprof.py -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_Master1 -t best:lnbnn_on=False,bar_l2_on=True --fsvx=0:1
# We want to query the oxford annots taged query
# and we want the database to contain
# K correct images per query, as well as the distractors
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db Oxford -a default:qhas_any=\(query,\),dpername=1,exclude_reference=True,minqual=ok
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db Oxford -a default:qhas_any=\(query,\),dpername=1,exclude_reference=True,minqual=good
python -m ibeis --tf get_annotcfg_list --db PZ_Master1 -a timectrl --acfginfo --verbtd --veryverbtd --nocache-aid
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST --disttype=ratio
Example:
>>> # SCRIPT
>>> from ibeis.expt.test_result import * # NOQA
>>> from ibeis.init import main_helpers
>>> disttype = ut.get_argval('--disttype', type_=list, default=None)
>>> ibs, testres = main_helpers.testdata_expts(
>>> defaultdb='PZ_MTEST', a=['timectrl'], t=['best'])
>>> f = ut.get_argval(('--filt', '-f'), type_=list, default=[''])
>>> testres.draw_feat_scoresep(f=f)
>>> ut.show_if_requested()
"""
print('[testres] draw_feat_scoresep')
import plottool as pt
def load_feat_scores(qreq_, qaids):
import ibeis # NOQA
from os.path import dirname, join # NOQA
# HACKY CACHE
cfgstr = qreq_.get_cfgstr(with_input=True)
cache_dir = join(dirname(dirname(ibeis.__file__)),
'TMP_FEATSCORE_CACHE')
namemode = ut.get_argval('--namemode', default=True)
fsvx = ut.get_argval('--fsvx',
type_='fuzzy_subset',
default=slice(None, None, None))
threshx = ut.get_argval('--threshx', type_=int, default=None)
thresh = ut.get_argval('--thresh', type_=float, default=.9)
num = ut.get_argval('--num', type_=int, default=1)
cfg_components = [
cfgstr, disttype, namemode, fsvx, threshx, thresh, f, num
]
cache_cfgstr = ','.join(ut.lmap(six.text_type, cfg_components))
cache_hashid = ut.hashstr27(cache_cfgstr + '_v1')
cache_name = ('get_cfgx_feat_scores_' + cache_hashid)
@ut.cached_func(cache_name,
cache_dir=cache_dir,
key_argx=[],
use_cache=True)
def get_cfgx_feat_scores(qreq_, qaids):
from ibeis.algo.hots import scorenorm
cm_list = qreq_.execute(qaids)
# print('Done loading cached chipmatches')
tup = scorenorm.get_training_featscores(qreq_,
cm_list,
disttype,
namemode,
fsvx,
threshx,
thresh,
num=num)
# print(ut.depth_profile(tup))
tp_scores, tn_scores, scorecfg = tup
return tp_scores, tn_scores, scorecfg
tp_scores, tn_scores, scorecfg = get_cfgx_feat_scores(qreq_, qaids)
return tp_scores, tn_scores, scorecfg
valid_case_pos = testres.case_sample2(filt_cfg=f, return_mask=False)
cfgx2_valid_qxs = ut.group_items(valid_case_pos.T[0], valid_case_pos.T[1])
test_qaids = testres.get_test_qaids()
cfgx2_valid_qaids = ut.map_dict_vals(ut.partial(ut.take, test_qaids),
cfgx2_valid_qxs)
join_acfgs = True
# TODO: option to average over pipeline configurations
if join_acfgs:
groupxs = testres.get_cfgx_groupxs()
else:
groupxs = list(zip(range(len(testres.cfgx2_qreq_))))
grouped_qreqs = ut.apply_grouping(testres.cfgx2_qreq_, groupxs)
grouped_scores = []
for cfgxs, qreq_group in zip(groupxs, grouped_qreqs):
# testres.print_pcfg_info()
score_group = []
for cfgx, qreq_ in zip(cfgxs, testres.cfgx2_qreq_):
print('Loading cached chipmatches')
qaids = cfgx2_valid_qaids[cfgx]
tp_scores, tn_scores, scorecfg = load_feat_scores(qreq_, qaids)
score_group.append((tp_scores, tn_scores, scorecfg))
grouped_scores.append(score_group)
cfgx2_shortlbl = testres.get_short_cfglbls(join_acfgs=join_acfgs)
for score_group, lbl in zip(grouped_scores, cfgx2_shortlbl):
tp_scores = np.hstack(ut.take_column(score_group, 0))
tn_scores = np.hstack(ut.take_column(score_group, 1))
scorecfg = '+++'.join(ut.unique(ut.take_column(score_group, 2)))
score_group
# TODO: learn this score normalizer as a model
# encoder = vt.ScoreNormalizer(adjust=4, monotonize=False)
encoder = vt.ScoreNormalizer(adjust=2, monotonize=True)
encoder.fit_partitioned(tp_scores, tn_scores, verbose=False)
figtitle = 'Feature Scores: %s, %s' % (scorecfg, lbl)
fnum = None
vizkw = {}
sephack = ut.get_argflag('--sephack')
if not sephack:
vizkw['target_tpr'] = .95
vizkw['score_range'] = (0, 1.0)
encoder.visualize(
figtitle=figtitle,
fnum=fnum,
with_scores=False,
#with_prebayes=True,
with_prebayes=False,
with_roc=True,
with_postbayes=False,
#with_postbayes=True,
**vizkw)
icon = testres.ibs.get_database_icon()
if icon is not None:
pt.overlay_icon(icon, coords=(1, 0), bbox_alignment=(1, 0))
if ut.get_argflag('--contextadjust'):
pt.adjust_subplots(left=.1, bottom=.25, wspace=.2, hspace=.2)
pt.adjust_subplots(use_argv=True)
return encoder
