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 test_siamese_performance(model, data, labels, flat_metadata, dataname=''):
r"""
CommandLine:
utprof.py -m ibeis_cnn --tf pz_patchmatch --db liberty --test --weights=liberty:current --arch=siaml2_128 --test
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure --weights=new
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --train --weights=new
python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128 --test # NOQA
python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128
"""
import vtool as vt
import plottool as pt
# TODO: save in model.trainind_dpath/diagnostics/figures
ut.colorprint('\n[siam_perf] Testing Siamese Performance', 'white')
#epoch_dpath = model.get_epoch_diagnostic_dpath()
epoch_dpath = model.arch_dpath
ut.vd(epoch_dpath)
dataname += ' ' + model.get_history_hashid() + '\n'
history_text = ut.list_str(model.era_history, newlines=True)
ut.write_to(ut.unixjoin(epoch_dpath, 'era_history.txt'), history_text)
#if True:
# import matplotlib as mpl
# mpl.rcParams['agg.path.chunksize'] = 100000
#data = data[::50]
#labels = labels[::50]
#from ibeis_cnn import utils
#data, labels = utils.random_xy_sample(data, labels, 10000, model.data_per_label_input)
FULL = not ut.get_argflag('--quick')
fnum_gen = pt.make_fnum_nextgen()
ut.colorprint('[siam_perf] Show era history', 'white')
fig = model.show_era_loss(fnum=fnum_gen())
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
# hack
ut.colorprint('[siam_perf] Show weights image', 'white')
fig = model.show_weights_image(fnum=fnum_gen())
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
#model.draw_all_conv_layer_weights(fnum=fnum_gen())
#model.imwrite_weights(1)
#model.imwrite_weights(2)
# Compute each type of score
ut.colorprint('[siam_perf] Building Scores', 'white')
test_outputs = model.predict2(model, data)
network_output = test_outputs['network_output_determ']
# hack converting network output to distances for non-descriptor networks
if len(network_output.shape) == 2 and network_output.shape[1] == 1:
cnn_scores = network_output.T[0]
elif len(network_output.shape) == 1:
cnn_scores = network_output
elif len(network_output.shape) == 2 and network_output.shape[1] > 1:
assert model.data_per_label_output == 2
vecs1 = network_output[0::2]
vecs2 = network_output[1::2]
cnn_scores = vt.L2(vecs1, vecs2)
else:
assert False
cnn_scores = cnn_scores.astype(np.float64)
# Segfaults with the data passed in is large (AND MEMMAPPED apparently)
# Fixed in hesaff implementation
SIFT = FULL
if SIFT:
sift_scores, sift_list = test_sift_patchmatch_scores(data, labels)
sift_scores = sift_scores.astype(np.float64)
ut.colorprint('[siam_perf] Learning Encoders', 'white')
# Learn encoders
encoder_kw = {
#'monotonize': False,
'monotonize': True,
}
cnn_encoder = vt.ScoreNormalizer(**encoder_kw)
cnn_encoder.fit(cnn_scores, labels)
if SIFT:
sift_encoder = vt.ScoreNormalizer(**encoder_kw)
sift_encoder.fit(sift_scores, labels)
# Visualize
ut.colorprint('[siam_perf] Visualize Encoders', 'white')
viz_kw = dict(
with_scores=False,
with_postbayes=False,
with_prebayes=False,
target_tpr=.95,
)
inter_cnn = cnn_encoder.visualize(
figtitle=dataname + ' CNN scores. #data=' + str(len(data)),
fnum=fnum_gen(), **viz_kw)
if SIFT:
inter_sift = sift_encoder.visualize(
figtitle=dataname + ' SIFT scores. #data=' + str(len(data)),
fnum=fnum_gen(), **viz_kw)
# Save
pt.save_figure(fig=inter_cnn.fig, dpath=epoch_dpath)
if SIFT:
pt.save_figure(fig=inter_sift.fig, dpath=epoch_dpath)
# Save out examples of hard errors
#cnn_fp_label_indicies, cnn_fn_label_indicies =
#cnn_encoder.get_error_indicies(cnn_scores, labels)
#sift_fp_label_indicies, sift_fn_label_indicies =
#sift_encoder.get_error_indicies(sift_scores, labels)
with_patch_examples = FULL
if with_patch_examples:
ut.colorprint('[siam_perf] Visualize Confusion Examples', 'white')
cnn_indicies = cnn_encoder.get_confusion_indicies(cnn_scores, labels)
if SIFT:
sift_indicies = sift_encoder.get_confusion_indicies(sift_scores, labels)
warped_patch1_list, warped_patch2_list = list(zip(*ut.ichunks(data, 2)))
samp_args = (warped_patch1_list, warped_patch2_list, labels)
_sample = functools.partial(draw_results.get_patch_sample_img, *samp_args)
cnn_fp_img = _sample({'fs': cnn_scores}, cnn_indicies.fp)[0]
cnn_fn_img = _sample({'fs': cnn_scores}, cnn_indicies.fn)[0]
cnn_tp_img = _sample({'fs': cnn_scores}, cnn_indicies.tp)[0]
cnn_tn_img = _sample({'fs': cnn_scores}, cnn_indicies.tn)[0]
if SIFT:
sift_fp_img = _sample({'fs': sift_scores}, sift_indicies.fp)[0]
sift_fn_img = _sample({'fs': sift_scores}, sift_indicies.fn)[0]
sift_tp_img = _sample({'fs': sift_scores}, sift_indicies.tp)[0]
sift_tn_img = _sample({'fs': sift_scores}, sift_indicies.tn)[0]
#if ut.show_was_requested():
#def rectify(arr):
# return np.flipud(arr)
SINGLE_FIG = False
if SINGLE_FIG:
def dump_img(img_, lbl, fnum):
fig, ax = pt.imshow(img_, figtitle=dataname + ' ' + lbl, fnum=fnum)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
dump_img(cnn_fp_img, 'cnn_fp_img', fnum_gen())
dump_img(cnn_fn_img, 'cnn_fn_img', fnum_gen())
dump_img(cnn_tp_img, 'cnn_tp_img', fnum_gen())
dump_img(cnn_tn_img, 'cnn_tn_img', fnum_gen())
dump_img(sift_fp_img, 'sift_fp_img', fnum_gen())
dump_img(sift_fn_img, 'sift_fn_img', fnum_gen())
dump_img(sift_tp_img, 'sift_tp_img', fnum_gen())
dump_img(sift_tn_img, 'sift_tn_img', fnum_gen())
#vt.imwrite(dataname + '_' + 'cnn_fp_img.png', (cnn_fp_img))
#vt.imwrite(dataname + '_' + 'cnn_fn_img.png', (cnn_fn_img))
#vt.imwrite(dataname + '_' + 'sift_fp_img.png', (sift_fp_img))
#vt.imwrite(dataname + '_' + 'sift_fn_img.png', (sift_fn_img))
else:
print('Drawing TP FP TN FN')
fnum = fnum_gen()
pnum_gen = pt.make_pnum_nextgen(4, 2)
fig = pt.figure(fnum)
pt.imshow(cnn_fp_img, title='CNN FP', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_fp_img, title='SIFT FP', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_fn_img, title='CNN FN', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_fn_img, title='SIFT FN', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_tp_img, title='CNN TP', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_tp_img, title='SIFT TP', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_tn_img, title='CNN TN', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_tn_img, title='SIFT TN', fnum=fnum, pnum=pnum_gen())
pt.set_figtitle(dataname + ' confusions')
pt.adjust_subplots(left=0, right=1.0, bottom=0., wspace=.01, hspace=.05)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))
with_patch_desc = FULL
if with_patch_desc:
ut.colorprint('[siam_perf] Visualize Patch Descriptors', 'white')
fnum = fnum_gen()
fig = pt.figure(fnum=fnum, pnum=(1, 1, 1))
num_rows = 7
pnum_gen = pt.make_pnum_nextgen(num_rows, 3)
# Compare actual output descriptors
for index in ut.random_indexes(len(sift_list), num_rows):
vec_sift = sift_list[index]
vec_cnn = network_output[index]
patch = data[index]
pt.imshow(patch, fnum=fnum, pnum=pnum_gen())
pt.plot_descriptor_signature(vec_cnn, 'cnn vec', fnum=fnum, pnum=pnum_gen())
pt.plot_sift_signature(vec_sift, 'sift vec', fnum=fnum, pnum=pnum_gen())
pt.set_figtitle('Patch Descriptors')
pt.adjust_subplots(left=0, right=0.95, bottom=0., wspace=.1, hspace=.15)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))
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 show_page(self, bring_to_front=False, onlyrows=None, fulldraw=True):
""" Plots all subaxes on a page
onlyrows is a hack to only draw a subset of the data again
"""
if ut.VERBOSE:
if not fulldraw:
print('[matchver] show_page(fulldraw=%r, onlyrows=%r)' %
(fulldraw, onlyrows))
else:
print('[matchver] show_page(fulldraw=%r)' % (fulldraw))
self.prepare_page(fulldraw=fulldraw)
# Variables we will work with to paint a pretty picture
ibs = self.ibs
nRows = self.nRows
colpad = 1 if self.cm is not None else 0
nCols = self.nCols + colpad
# Distinct color for every unique name
unique_nids = ut.unique_ordered(
ibs.get_annot_name_rowids(self.all_aid_list,
distinguish_unknowns=False))
unique_colors = pt.distinct_colors(len(unique_nids),
brightness=.7,
hue_range=(.05, .95))
self.nid2_color = dict(zip(unique_nids, unique_colors))
row_aids_list = self.get_row_aids_list()
if self.cm is not None:
print("DRAWING QRES")
pnum = (1, nCols, 1)
if not fulldraw:
# not doing full draw so we have to clear any axes
# that are here already manually
ax = self.fig.add_subplot(*pnum)
self.clear_parent_axes(ax)
self.cm.show_single_annotmatch(self.qreq_,
self.aid2,
fnum=self.fnum,
pnum=pnum,
draw_fmatch=True,
colorbar_=False)
# For each row
for rowx, aid_list in enumerate(row_aids_list):
offset = rowx * nCols + 1
if onlyrows is not None and rowx not in onlyrows:
continue
#ibsfuncs.assert_valid_aids(ibs, groundtruth)
# For each column
for colx, aid in enumerate(aid_list, start=colpad):
if colx >= nCols:
break
try:
nid = ibs.get_annot_name_rowids(aid)
if ibsfuncs.is_nid_unknown(ibs, [nid])[0]:
color = const.UNKNOWN_PURPLE_RGBA01
else:
color = self.nid2_color[nid]
except Exception as ex:
ut.printex(ex)
print('nid = %r' % (nid, ))
print('self.nid2_color = %s' %
(ut.repr2(self.nid2_color), ))
raise
px = colx + offset
ax = self.plot_chip(int(aid),
nRows,
nCols,
px,
color=color,
fulldraw=fulldraw)
# If there are still more in this row to display
if colx + 1 < len(aid_list) and colx + 1 >= nCols:
total_indices = len(aid_list)
current_index = self.col_offset_list[rowx] + 1
next_text = 'next\n%d/%d' % (current_index, total_indices)
next_func = functools.partial(self.rotate_row, rowx=rowx)
self.append_button(next_text,
callback=next_func,
location='right',
size='33%',
ax=ax)
if fulldraw:
self.show_hud()
hspace = .05 if (self.nCols) > 1 else .1
subplotspar = {
'left': .1,
'right': .9,
'top': .85,
'bottom': .1,
'wspace': .3,
'hspace': hspace,
}
pt.adjust_subplots(**subplotspar)
self.draw()
self.show()
if bring_to_front:
self.bring_to_front()
def _model_data_flow_to_networkx(model_info):
layers = model_info['layer']
import networkx as nx
G = nx.DiGraph()
prev = None
# Stores last node with the data for this layer in it
prev_map = {}
SHOW_LOOPS = False
for layer in layers:
name = layer.get('name')
print('name = {!r}'.format(name))
G.add_node(name)
bottom = set(layer.get('bottom', []))
top = set(layer.get('top', []))
both = top.intersection(bottom)
if both:
if prev is None:
prev = both
for b in both:
prev_map[b] = name
for b in prev:
print(' * b = {!r}'.format(b))
G.add_edge(b, name, constraint=False)
for b in both:
print(' * b = {!r}'.format(b))
kw = {}
if not G.has_edge(b, name):
kw['color'] = 'red'
G.add_edge(b, name, constraint=True, **kw)
prev = [name]
else:
prev = None
# for b in (bottom - both):
for b in bottom:
print(' * b = {!r}'.format(b))
constraint = True
G.add_edge(prev_map.get(b, b), name, constraint=constraint)
if SHOW_LOOPS:
G.add_edge(b, name)
# for t in (bottom - top):
for t in top:
print(' * t = {!r}'.format(t))
constraint = True
G.add_edge(name, prev_map.get(t, t), constraint=constraint)
if SHOW_LOOPS:
G.add_edge(name, t)
G.remove_edges_from(list(G.selfloop_edges()))
import plottool as pt
pt.qtensure()
pt.show_nx(G, arrow_width=1)
pt.adjust_subplots(left=0, right=1, top=1, bottom=0)
pt.pan_factory()
pt.zoom_factory()
list(nx.topological_sort(G))
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
def show_graph(infr, title, final=False, selected_edges=None):
if not VISUALIZE:
return
# TODO: rich colored text?
latest = '\n'.join(infr.latest_logs())
showkw = dict(
# fontsize=infr.graph.graph['fontsize'],
# fontname=infr.graph.graph['fontname'],
show_unreviewed_edges=True,
show_inferred_same=False,
show_inferred_diff=False,
outof=(len(infr.aids)),
# show_inferred_same=True,
# show_inferred_diff=True,
selected_edges=selected_edges,
show_labels=True,
simple_labels=True,
# show_recent_review=not final,
show_recent_review=False,
# splines=infr.graph.graph['splines'],
reposition=False,
# with_colorbar=True
)
verbose = infr.verbose
infr.verbose = 0
infr_ = infr.copy()
infr_ = infr
infr_.verbose = verbose
infr_.show(pickable=True, verbose=0, **showkw)
infr.verbose = verbose
# print('status ' + ut.repr4(infr_.status()))
# infr.show(**showkw)
ax = pt.gca()
pt.set_title(title, fontsize=20)
fig = pt.gcf()
fontsize = 22
if True:
# postprocess xlabel
lines = []
for line in latest.split('\n'):
if False and line.startswith('ORACLE ERROR'):
lines += ['ORACLE ERROR']
else:
lines += [line]
latest = '\n'.join(lines)
if len(lines) > 10:
fontsize = 16
if len(lines) > 12:
fontsize = 14
if len(lines) > 14:
fontsize = 12
if len(lines) > 18:
fontsize = 10
if len(lines) > 23:
fontsize = 8
if True:
pt.adjust_subplots(top=.95, left=0, right=1, bottom=.45, fig=fig)
ax.set_xlabel('\n' + latest)
xlabel = ax.get_xaxis().get_label()
xlabel.set_horizontalalignment('left')
# xlabel.set_x(.025)
xlabel.set_x(-.6)
# xlabel.set_fontname('CMU Typewriter Text')
xlabel.set_fontname('Inconsolata')
xlabel.set_fontsize(fontsize)
ax.set_aspect('equal')
# ax.xaxis.label.set_color('red')
from os.path import join
fpath = join(dpath, 'demo_{:04d}.png'.format(next(fig_counter)))
fig.savefig(
fpath,
dpi=300,
# transparent=True,
edgecolor='none')
# pt.save_figure(dpath=dpath, dpi=300)
infr.latest_logs()
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)]
from ibeis.scripts.thesis import TMP_RC
import matplotlib as mpl
mpl.rcParams.update(TMP_RC)
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 False and 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))
pt.overlay_icon(icon,
coords=(0, 1),
bbox_alignment=(0, 1),
as_artist=1,
max_asize=(100, 200))
#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_subplots(left=.08, bottom=.1, top=.9, wspace=.3, hspace=.1)
pt.adjust_subplots(use_argv=True)