def run_until_name_decision_signal(ibs, cm, qreq_, incinfo=None):
r"""
DECISION STEP 1)
Either the system or the user makes a decision about the name of the query
annotation.
CommandLine:
python -m ibeis.algo.hots.automated_matcher --test-run_until_name_decision_signal
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automated_matcher import * # NOQA
>>> ibs, qaid_chunk = testdata_automatch()
>>> exemplar_aids = ibs.get_valid_aids(is_exemplar=True)
>>> incinfo = {}
>>> gen = generate_subquery_steps(ibs, qaid_chunk, incinfo)
>>> item = six.next(gen)
>>> ibs, cm, qreq_, incinfo = item
>>> # verify results
>>> run_until_name_decision_signal(ibs, cm, qreq_, incinfo)
Ignore::
cm.ishow_top(ibs, sidebyside=False, show_query=True)
"""
print('--- Identifying Query Animal ---')
qaid = cm.qaid
choicetup = automatch_suggestor.get_qres_name_choices(ibs, cm)
name_suggest_tup = get_name_suggestion(ibs, qaid, choicetup, incinfo)
# Have the system ask the user if it is not confident in its decision
autoname_msg, chosen_names, name_confidence = name_suggest_tup
print('autoname_msg=')
print(autoname_msg)
print('... checking confidence=%r in name decision.' % (name_confidence, ))
if name_confidence < incinfo.get('name_confidence_thresh', 1.0):
print('... confidence is too low. need user input')
if incinfo.get('interactive', False):
print('... asking user for input')
if qreq_.normalizer is not None:
VIZ_SCORE_NORM = False
#VIZ_SCORE_NORM = ut.is_developer()
if VIZ_SCORE_NORM:
qreq_.normalizer.visualize(fnum=511, verbose=False)
user_dialogs.wait_for_user_name_decision(ibs,
cm,
qreq_,
choicetup,
name_suggest_tup,
incinfo=incinfo)
else:
run_until_finish(incinfo=incinfo)
print('... cannot ask user for input. doing nothing')
else:
print('... confidence is above threshold. Making decision')
#return ('CALLBACK', chosen_names)
name_decision_callback = incinfo['name_decision_callback']
name_decision_callback(chosen_names)
def run_until_name_decision_signal(ibs, cm, qreq_, incinfo=None):
r"""
DECISION STEP 1)
Either the system or the user makes a decision about the name of the query
annotation.
CommandLine:
python -m ibeis.algo.hots.automated_matcher --test-run_until_name_decision_signal
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automated_matcher import * # NOQA
>>> ibs, qaid_chunk = testdata_automatch()
>>> exemplar_aids = ibs.get_valid_aids(is_exemplar=True)
>>> incinfo = {}
>>> gen = generate_subquery_steps(ibs, qaid_chunk, incinfo)
>>> item = six.next(gen)
>>> ibs, cm, qreq_, incinfo = item
>>> # verify results
>>> run_until_name_decision_signal(ibs, cm, qreq_, incinfo)
Ignore::
cm.ishow_top(ibs, sidebyside=False, show_query=True)
"""
print('--- Identifying Query Animal ---')
qaid = cm.qaid
choicetup = automatch_suggestor.get_qres_name_choices(ibs, cm)
name_suggest_tup = get_name_suggestion(ibs, qaid, choicetup, incinfo)
# Have the system ask the user if it is not confident in its decision
autoname_msg, chosen_names, name_confidence = name_suggest_tup
print('autoname_msg=')
print(autoname_msg)
print('... checking confidence=%r in name decision.' % (name_confidence,))
if name_confidence < incinfo.get('name_confidence_thresh', 1.0):
print('... confidence is too low. need user input')
if incinfo.get('interactive', False):
print('... asking user for input')
if qreq_.normalizer is not None:
VIZ_SCORE_NORM = False
#VIZ_SCORE_NORM = ut.is_developer()
if VIZ_SCORE_NORM:
qreq_.normalizer.visualize(fnum=511, verbose=False)
user_dialogs.wait_for_user_name_decision(ibs, cm, qreq_, choicetup,
name_suggest_tup,
incinfo=incinfo)
else:
run_until_finish(incinfo=incinfo)
print('... cannot ask user for input. doing nothing')
else:
print('... confidence is above threshold. Making decision')
#return ('CALLBACK', chosen_names)
name_decision_callback = incinfo['name_decision_callback']
name_decision_callback(chosen_names)
def update_normalizer(ibs, cm, qreq_, chosen_names):
r"""
adds new support data to the current normalizer
FIXME: broken
Args:
ibs (IBEISController): ibeis controller object
qreq_ (QueryRequest): query request object with hyper-parameters
choicetup (?):
name (?):
Returns:
tuple: (tp_rawscore, tn_rawscore)
CommandLine:
python -m ibeis.algo.hots.automated_matcher --test-update_normalizer
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automated_matcher import * # NOQA
>>> ibs, qaid_chunk = testdata_automatch()
>>> exemplar_aids = ibs.get_valid_aids(is_exemplar=True)
>>> incinfo = {}
>>> gen = generate_subquery_steps(ibs, qaid_chunk, incinfo)
>>> item = six.next(gen)
>>> ibs, cm, qreq_, incinfo = item
>>> qreq_.load_score_normalizer()
>>> # verify results
>>> chosen_names = ['easy']
>>> update_normalizer(ibs, cm, qreq_, chosen_names)
"""
# Fixme: duplicate call to get_qres_name_choices
if qreq_.normalizer is None:
print('[update_normalizer] NOT UPDATING. qreq_ has not loaded a score normalizer')
return
if len(chosen_names) != 1:
print('[update_normalizer] NOT UPDATING. only updates using simple matches')
return
qaid = cm.qaid
choicetup = automatch_suggestor.get_qres_name_choices(ibs, cm)
(sorted_nids, sorted_nscore, sorted_rawscore, sorted_aids, sorted_ascores) = choicetup
# Get new True Negative support data for score normalization
name = chosen_names[0]
rank = ut.listfind(ibs.get_name_texts(sorted_nids), name)
if rank is None:
return
nid = sorted_nids[rank]
tp_rawscore = sorted_rawscore[rank]
valid_falseranks = set(range(len(sorted_rawscore))) - set([rank])
if len(valid_falseranks) > 0:
tn_rank = min(valid_falseranks)
tn_rawscore = sorted_rawscore[tn_rank][0]
else:
tn_rawscore = None
#return tp_rawscore, tn_rawscore
canupdate = tp_rawscore is not None and tn_rawscore is not None
if canupdate:
# TODO: UPDATE SCORE NORMALIZER HERE
print('UPDATING! NORMALIZER')
tp_labels = [ut.deterministic_uuid((qaid, nid))]
tn_labels = [ut.deterministic_uuid((qaid, nid))]
print('new normalization example: tp_rawscore={}, tn_rawscore={}'.format(tp_rawscore, tn_rawscore))
print('new normalization example: tp_labels={}, tn_labels={}'.format(tp_labels, tn_labels))
tp_scores = [tp_rawscore]
tn_scores = [tn_rawscore]
qreq_.normalizer.add_support(tp_scores, tn_scores, tp_labels, tn_labels)
qreq_.normalizer.retrain()
species_text = '_'.join(qreq_.get_unique_species()) # HACK
# TODO: figure out where to store normalizer
qreq_.normalizer.save(ibs.get_local_species_scorenorm_cachedir(species_text))
else:
print('NOUPDATE! cannot update score normalization')
def update_normalizer(ibs, cm, qreq_, chosen_names):
r"""
adds new support data to the current normalizer
FIXME: broken
Args:
ibs (IBEISController): ibeis controller object
qreq_ (QueryRequest): query request object with hyper-parameters
choicetup (?):
name (?):
Returns:
tuple: (tp_rawscore, tn_rawscore)
CommandLine:
python -m ibeis.algo.hots.automated_matcher --test-update_normalizer
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automated_matcher import * # NOQA
>>> ibs, qaid_chunk = testdata_automatch()
>>> exemplar_aids = ibs.get_valid_aids(is_exemplar=True)
>>> incinfo = {}
>>> gen = generate_subquery_steps(ibs, qaid_chunk, incinfo)
>>> item = six.next(gen)
>>> ibs, cm, qreq_, incinfo = item
>>> qreq_.load_score_normalizer()
>>> # verify results
>>> chosen_names = ['easy']
>>> update_normalizer(ibs, cm, qreq_, chosen_names)
"""
# Fixme: duplicate call to get_qres_name_choices
if qreq_.normalizer is None:
print(
'[update_normalizer] NOT UPDATING. qreq_ has not loaded a score normalizer'
)
return
if len(chosen_names) != 1:
print(
'[update_normalizer] NOT UPDATING. only updates using simple matches'
)
return
qaid = cm.qaid
choicetup = automatch_suggestor.get_qres_name_choices(ibs, cm)
(sorted_nids, sorted_nscore, sorted_rawscore, sorted_aids,
sorted_ascores) = choicetup
# Get new True Negative support data for score normalization
name = chosen_names[0]
rank = ut.listfind(ibs.get_name_texts(sorted_nids), name)
if rank is None:
return
nid = sorted_nids[rank]
tp_rawscore = sorted_rawscore[rank]
valid_falseranks = set(range(len(sorted_rawscore))) - set([rank])
if len(valid_falseranks) > 0:
tn_rank = min(valid_falseranks)
tn_rawscore = sorted_rawscore[tn_rank][0]
else:
tn_rawscore = None
#return tp_rawscore, tn_rawscore
canupdate = tp_rawscore is not None and tn_rawscore is not None
if canupdate:
# TODO: UPDATE SCORE NORMALIZER HERE
print('UPDATING! NORMALIZER')
tp_labels = [ut.deterministic_uuid((qaid, nid))]
tn_labels = [ut.deterministic_uuid((qaid, nid))]
print(
'new normalization example: tp_rawscore={}, tn_rawscore={}'.format(
tp_rawscore, tn_rawscore))
print('new normalization example: tp_labels={}, tn_labels={}'.format(
tp_labels, tn_labels))
tp_scores = [tp_rawscore]
tn_scores = [tn_rawscore]
qreq_.normalizer.add_support(tp_scores, tn_scores, tp_labels,
tn_labels)
qreq_.normalizer.retrain()
species_text = '_'.join(qreq_.get_unique_species()) # HACK
# TODO: figure out where to store normalizer
qreq_.normalizer.save(
ibs.get_local_species_scorenorm_cachedir(species_text))
else:
print('NOUPDATE! cannot update score normalization')
