def test_config(qreq_, qres_orig, cfgdict):
""" function to grid search over """
qres_copy = copy.deepcopy(qres_orig)
qreq_vsone_ = qreq_
qres_vsone = qres_copy
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
newfsv_list, newscore_aids = special_query.get_extern_distinctiveness(qreq_, qres_copy, **cfgdict)
special_query.apply_new_qres_filter_scores(qreq_vsone_, qres_vsone, newfsv_list, newscore_aids, filtkey)
tp_score = qres_copy.aid2_score[aid2]
tn_score = qres_copy.aid2_score[tn_aid]
return qres_copy, tp_score, tn_score
def try_config(qreq_, qres_orig, cfgdict):
""" function to grid search over """
qres_copy = copy.deepcopy(qres_orig)
qreq_vsone_ = qreq_
qres_vsone = qres_copy
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
newfsv_list, newscore_aids = special_query.get_extern_distinctiveness(qreq_, qres_copy, **cfgdict)
special_query.apply_new_qres_filter_scores(qreq_vsone_, qres_vsone, newfsv_list, newscore_aids, filtkey)
tp_score = qres_copy.aid2_score[aid2]
tn_score = qres_copy.aid2_score[tn_aid]
return qres_copy, tp_score, tn_score
def myquery():
r"""
BUG::
THERE IS A BUG SOMEWHERE: HOW IS THIS POSSIBLE?
if everything is weightd ) how di the true positive even get a score
while the true negative did not
qres_copy.filtkey_list = ['ratio', 'fg', 'homogerr', 'distinctiveness']
CORRECT STATS
{
'max' : [0.832, 0.968, 0.604, 0.000],
'min' : [0.376, 0.524, 0.000, 0.000],
'mean' : [0.561, 0.924, 0.217, 0.000],
'std' : [0.114, 0.072, 0.205, 0.000],
'nMin' : [1, 1, 1, 51],
'nMax' : [1, 1, 1, 1],
'shape': (52, 4),
}
INCORRECT STATS
{
'max' : [0.759, 0.963, 0.264, 0.000],
'min' : [0.379, 0.823, 0.000, 0.000],
'mean' : [0.506, 0.915, 0.056, 0.000],
'std' : [0.125, 0.039, 0.078, 0.000],
'nMin' : [1, 1, 1, 24],
'nMax' : [1, 1, 1, 1],
'shape': (26, 4),
# score_diff, tp_score, tn_score, p, K, dcvs_clip_max, fg_power, homogerr_power
0.494, 0.494, 0.000, 73.000, 2, 0.500, 0.100, 10.000
see how seperability changes as we very things
CommandLine:
python -m ibeis.algo.hots.devcases --test-myquery
python -m ibeis.algo.hots.devcases --test-myquery --show --index 0
python -m ibeis.algo.hots.devcases --test-myquery --show --index 1
python -m ibeis.algo.hots.devcases --test-myquery --show --index 2
References:
http://en.wikipedia.org/wiki/Pareto_distribution <- look into
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.all_imports import * # NOQA
>>> from ibeis.algo.hots.devcases import * # NOQA
>>> ut.dev_ipython_copypaster(myquery) if ut.inIPython() else myquery()
>>> pt.show_if_requested()
"""
from ibeis.algo.hots import special_query # NOQA
from ibeis.algo.hots import distinctiveness_normalizer # NOQA
from ibeis import viz # NOQA
import plottool as pt
index = ut.get_argval('--index', int, 0)
ibs, aid1, aid2, tn_aid = testdata_my_exmaples(index)
qaids = [aid1]
daids = [aid2] + [tn_aid]
qvuuid = ibs.get_annot_visual_uuids(aid1)
cfgdict_vsone = dict(
sv_on=True,
#sv_on=False,
#codename='vsone_unnorm_dist_ratio_extern_distinctiveness',
codename='vsone_unnorm_ratio_extern_distinctiveness',
sver_output_weighting=True,
)
use_cache = False
save_qcache = False
qres_list, qreq_ = ibs.query_chips(qaids,
daids,
cfgdict=cfgdict_vsone,
return_request=True,
use_cache=use_cache,
save_qcache=save_qcache,
verbose=True)
qreq_.load_distinctiveness_normalizer()
qres = qres_list[0]
top_aids = qres.get_top_aids() # NOQA
qres_orig = qres # NOQA
def test_config(qreq_, qres_orig, cfgdict):
""" function to grid search over """
qres_copy = copy.deepcopy(qres_orig)
qreq_vsone_ = qreq_
qres_vsone = qres_copy
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
newfsv_list, newscore_aids = special_query.get_extern_distinctiveness(
qreq_, qres_copy, **cfgdict)
special_query.apply_new_qres_filter_scores(qreq_vsone_, qres_vsone,
newfsv_list, newscore_aids,
filtkey)
tp_score = qres_copy.aid2_score[aid2]
tn_score = qres_copy.aid2_score[tn_aid]
return qres_copy, tp_score, tn_score
#[.01, .1, .2, .5, .6, .7, .8, .9, 1.0]),
#FiltKeys = hstypes.FiltKeys
# FIXME: Use other way of doing gridsearch
grid_basis = distinctiveness_normalizer.DCVS_DEFAULT.get_grid_basis()
gridsearch = ut.GridSearch(grid_basis, label='qvuuid=%r' % (qvuuid, ))
print('Begin Grid Search')
for cfgdict in ut.ProgressIter(gridsearch, lbl='GridSearch'):
qres_copy, tp_score, tn_score = test_config(qreq_, qres_orig, cfgdict)
gridsearch.append_result(tp_score, tn_score)
print('Finish Grid Search')
# Get best result
best_cfgdict = gridsearch.get_rank_cfgdict()
qres_copy, tp_score, tn_score = test_config(qreq_, qres_orig, best_cfgdict)
# Examine closely what you can do with scores
if False:
qres_copy = copy.deepcopy(qres_orig)
qreq_vsone_ = qreq_
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
newfsv_list, newscore_aids = special_query.get_extern_distinctiveness(
qreq_, qres_copy, **cfgdict)
ut.embed()
def make_cm_very_old_tuple(qres_copy):
assert ut.listfind(qres_copy.filtkey_list, filtkey) is None
weight_filters = hstypes.WEIGHT_FILTERS
weight_filtxs, nonweight_filtxs = special_query.index_partition(
qres_copy.filtkey_list, weight_filters)
aid2_fsv = {}
aid2_fs = {}
aid2_score = {}
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
pass
break
#scorex_vsone = ut.listfind(qres_copy.filtkey_list, filtkey)
#if scorex_vsone is None:
# TODO: add spatial verification as a filter score
# augment the vsone scores
# TODO: paramaterize
weighted_ave_score = True
if weighted_ave_score:
# weighted average scoring
new_fs_vsone = special_query.weighted_average_scoring(
new_fsv_vsone, weight_filtxs, nonweight_filtxs)
else:
# product scoring
new_fs_vsone = special_query.product_scoring(new_fsv_vsone)
new_score_vsone = new_fs_vsone.sum()
aid2_fsv[daid] = new_fsv_vsone
aid2_fs[daid] = new_fs_vsone
aid2_score[daid] = new_score_vsone
return aid2_fsv, aid2_fs, aid2_score
# Look at plot of query products
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
new_fs_vsone = special_query.product_scoring(new_fsv_vsone)
scores_list = np.array(new_fs_vsone)[:, None].T
pt.plot_sorted_scores(scores_list,
logscale=False,
figtitle=str(daid))
pt.iup()
special_query.apply_new_qres_filter_scores(qreq_vsone_, qres_copy,
newfsv_list, newscore_aids,
filtkey)
# PRINT INFO
import functools
#ut.rrrr()
get_stats_str = functools.partial(ut.get_stats_str,
axis=0,
newlines=True,
precision=3)
tp_stats_str = ut.align(get_stats_str(qres_copy.aid2_fsv[aid2]), ':')
tn_stats_str = ut.align(get_stats_str(qres_copy.aid2_fsv[tn_aid]), ':')
info_str_list = []
info_str_list.append('qres_copy.filtkey_list = %r' %
(qres_copy.filtkey_list, ))
info_str_list.append('CORRECT STATS')
info_str_list.append(tp_stats_str)
info_str_list.append('INCORRECT STATS')
info_str_list.append(tn_stats_str)
info_str = '\n'.join(info_str_list)
print(info_str)
# SHOW BEST RESULT
#qres_copy.ishow_top(ibs, fnum=pt.next_fnum())
#qres_orig.ishow_top(ibs, fnum=pt.next_fnum())
# Text Informatio
param_lbl = 'dcvs_power'
param_stats_str = gridsearch.get_dimension_stats_str(param_lbl)
print(param_stats_str)
csvtext = gridsearch.get_csv_results(10)
print(csvtext)
# Paramter visuzliation
fnum = pt.next_fnum()
# plot paramter influence
param_label_list = gridsearch.get_param_lbls()
pnum_ = pt.get_pnum_func(2, len(param_label_list))
for px, param_label in enumerate(param_label_list):
gridsearch.plot_dimension(param_label, fnum=fnum, pnum=pnum_(px))
# plot match figure
pnum2_ = pt.get_pnum_func(2, 2)
qres_copy.show_matches(ibs, aid2, fnum=fnum, pnum=pnum2_(2))
qres_copy.show_matches(ibs, tn_aid, fnum=fnum, pnum=pnum2_(3))
# Add figure labels
figtitle = 'Effect of parameters on vsone separation for a single case'
subtitle = 'qvuuid = %r' % (qvuuid)
figtitle += '\n' + subtitle
pt.set_figtitle(figtitle)
# Save Figure
#fig_fpath = pt.save_figure(usetitle=True)
#print(fig_fpath)
# Write CSV Results
#csv_fpath = fig_fpath + '.csv.txt'
#ut.write_to(csv_fpath, csvtext)
#qres_copy.ishow_top(ibs)
#from matplotlib import pyplot as plt
#plt.show()
#print(ut.list_str()))
# TODO: plot max variation dims
#import plottool as pt
#pt.plot(p_list, diff_list)
"""
def myquery():
r"""
BUG::
THERE IS A BUG SOMEWHERE: HOW IS THIS POSSIBLE?
if everything is weightd ) how di the true positive even get a score
while the true negative did not
qres_copy.filtkey_list = ['ratio', 'fg', 'homogerr', 'distinctiveness']
CORRECT STATS
{
'max' : [0.832, 0.968, 0.604, 0.000],
'min' : [0.376, 0.524, 0.000, 0.000],
'mean' : [0.561, 0.924, 0.217, 0.000],
'std' : [0.114, 0.072, 0.205, 0.000],
'nMin' : [1, 1, 1, 51],
'nMax' : [1, 1, 1, 1],
'shape': (52, 4),
}
INCORRECT STATS
{
'max' : [0.759, 0.963, 0.264, 0.000],
'min' : [0.379, 0.823, 0.000, 0.000],
'mean' : [0.506, 0.915, 0.056, 0.000],
'std' : [0.125, 0.039, 0.078, 0.000],
'nMin' : [1, 1, 1, 24],
'nMax' : [1, 1, 1, 1],
'shape': (26, 4),
# score_diff, tp_score, tn_score, p, K, dcvs_clip_max, fg_power, homogerr_power
0.494, 0.494, 0.000, 73.000, 2, 0.500, 0.100, 10.000
see how seperability changes as we very things
CommandLine:
python -m ibeis.algo.hots.devcases --test-myquery
python -m ibeis.algo.hots.devcases --test-myquery --show --index 0
python -m ibeis.algo.hots.devcases --test-myquery --show --index 1
python -m ibeis.algo.hots.devcases --test-myquery --show --index 2
References:
http://en.wikipedia.org/wiki/Pareto_distribution <- look into
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.all_imports import * # NOQA
>>> from ibeis.algo.hots.devcases import * # NOQA
>>> ut.dev_ipython_copypaster(myquery) if ut.inIPython() else myquery()
>>> pt.show_if_requested()
"""
from ibeis.algo.hots import special_query # NOQA
from ibeis.algo.hots import distinctiveness_normalizer # NOQA
from ibeis import viz # NOQA
import plottool as pt
index = ut.get_argval('--index', int, 0)
ibs, aid1, aid2, tn_aid = testdata_my_exmaples(index)
qaids = [aid1]
daids = [aid2] + [tn_aid]
qvuuid = ibs.get_annot_visual_uuids(aid1)
cfgdict_vsone = dict(
sv_on=True,
#sv_on=False,
#codename='vsone_unnorm_dist_ratio_extern_distinctiveness',
codename='vsone_unnorm_ratio_extern_distinctiveness',
sver_output_weighting=True,
)
use_cache = False
save_qcache = False
qres_list, qreq_ = ibs.query_chips(qaids, daids, cfgdict=cfgdict_vsone,
return_request=True, use_cache=use_cache,
save_qcache=save_qcache, verbose=True)
qreq_.load_distinctiveness_normalizer()
qres = qres_list[0]
top_aids = qres.get_top_aids() # NOQA
qres_orig = qres # NOQA
def test_config(qreq_, qres_orig, cfgdict):
""" function to grid search over """
qres_copy = copy.deepcopy(qres_orig)
qreq_vsone_ = qreq_
qres_vsone = qres_copy
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
newfsv_list, newscore_aids = special_query.get_extern_distinctiveness(qreq_, qres_copy, **cfgdict)
special_query.apply_new_qres_filter_scores(qreq_vsone_, qres_vsone, newfsv_list, newscore_aids, filtkey)
tp_score = qres_copy.aid2_score[aid2]
tn_score = qres_copy.aid2_score[tn_aid]
return qres_copy, tp_score, tn_score
#[.01, .1, .2, .5, .6, .7, .8, .9, 1.0]),
#FiltKeys = hstypes.FiltKeys
# FIXME: Use other way of doing gridsearch
grid_basis = distinctiveness_normalizer.DCVS_DEFAULT.get_grid_basis()
gridsearch = ut.GridSearch(grid_basis, label='qvuuid=%r' % (qvuuid,))
print('Begin Grid Search')
for cfgdict in ut.ProgressIter(gridsearch, lbl='GridSearch'):
qres_copy, tp_score, tn_score = test_config(qreq_, qres_orig, cfgdict)
gridsearch.append_result(tp_score, tn_score)
print('Finish Grid Search')
# Get best result
best_cfgdict = gridsearch.get_rank_cfgdict()
qres_copy, tp_score, tn_score = test_config(qreq_, qres_orig, best_cfgdict)
# Examine closely what you can do with scores
if False:
qres_copy = copy.deepcopy(qres_orig)
qreq_vsone_ = qreq_
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
newfsv_list, newscore_aids = special_query.get_extern_distinctiveness(qreq_, qres_copy, **cfgdict)
ut.embed()
def make_cm_very_old_tuple(qres_copy):
assert ut.listfind(qres_copy.filtkey_list, filtkey) is None
weight_filters = hstypes.WEIGHT_FILTERS
weight_filtxs, nonweight_filtxs = special_query.index_partition(qres_copy.filtkey_list, weight_filters)
aid2_fsv = {}
aid2_fs = {}
aid2_score = {}
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
pass
break
#scorex_vsone = ut.listfind(qres_copy.filtkey_list, filtkey)
#if scorex_vsone is None:
# TODO: add spatial verification as a filter score
# augment the vsone scores
# TODO: paramaterize
weighted_ave_score = True
if weighted_ave_score:
# weighted average scoring
new_fs_vsone = special_query.weighted_average_scoring(new_fsv_vsone, weight_filtxs, nonweight_filtxs)
else:
# product scoring
new_fs_vsone = special_query.product_scoring(new_fsv_vsone)
new_score_vsone = new_fs_vsone.sum()
aid2_fsv[daid] = new_fsv_vsone
aid2_fs[daid] = new_fs_vsone
aid2_score[daid] = new_score_vsone
return aid2_fsv, aid2_fs, aid2_score
# Look at plot of query products
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
new_fs_vsone = special_query.product_scoring(new_fsv_vsone)
scores_list = np.array(new_fs_vsone)[:, None].T
pt.plot_sorted_scores(scores_list, logscale=False, figtitle=str(daid))
pt.iup()
special_query.apply_new_qres_filter_scores(qreq_vsone_, qres_copy, newfsv_list, newscore_aids, filtkey)
# PRINT INFO
import functools
#ut.rrrr()
get_stats_str = functools.partial(ut.get_stats_str, axis=0, newlines=True, precision=3)
tp_stats_str = ut.align(get_stats_str(qres_copy.aid2_fsv[aid2]), ':')
tn_stats_str = ut.align(get_stats_str(qres_copy.aid2_fsv[tn_aid]), ':')
info_str_list = []
info_str_list.append('qres_copy.filtkey_list = %r' % (qres_copy.filtkey_list,))
info_str_list.append('CORRECT STATS')
info_str_list.append(tp_stats_str)
info_str_list.append('INCORRECT STATS')
info_str_list.append(tn_stats_str)
info_str = '\n'.join(info_str_list)
print(info_str)
# SHOW BEST RESULT
#qres_copy.ishow_top(ibs, fnum=pt.next_fnum())
#qres_orig.ishow_top(ibs, fnum=pt.next_fnum())
# Text Informatio
param_lbl = 'dcvs_power'
param_stats_str = gridsearch.get_dimension_stats_str(param_lbl)
print(param_stats_str)
csvtext = gridsearch.get_csv_results(10)
print(csvtext)
# Paramter visuzliation
fnum = pt.next_fnum()
# plot paramter influence
param_label_list = gridsearch.get_param_lbls()
pnum_ = pt.get_pnum_func(2, len(param_label_list))
for px, param_label in enumerate(param_label_list):
gridsearch.plot_dimension(param_label, fnum=fnum, pnum=pnum_(px))
# plot match figure
pnum2_ = pt.get_pnum_func(2, 2)
qres_copy.show_matches(ibs, aid2, fnum=fnum, pnum=pnum2_(2))
qres_copy.show_matches(ibs, tn_aid, fnum=fnum, pnum=pnum2_(3))
# Add figure labels
figtitle = 'Effect of parameters on vsone separation for a single case'
subtitle = 'qvuuid = %r' % (qvuuid)
figtitle += '\n' + subtitle
pt.set_figtitle(figtitle)
# Save Figure
#fig_fpath = pt.save_figure(usetitle=True)
#print(fig_fpath)
# Write CSV Results
#csv_fpath = fig_fpath + '.csv.txt'
#ut.write_to(csv_fpath, csvtext)
#qres_copy.ishow_top(ibs)
#from matplotlib import pyplot as plt
#plt.show()
#print(ut.list_str()))
# TODO: plot max variation dims
#import plottool as pt
#pt.plot(p_list, diff_list)
"""
