def learn_evaluation_verifiers(infr):
"""
Creates a cross-validated ensemble of classifiers to evaluate
verifier error cases and groundtruth errors.
Doctest:
>>> import ibeis
>>> infr = ibeis.AnnotInference(
>>> 'PZ_MTEST', aids='all', autoinit='annotmatch',
>>> verbose=4)
>>> verifiers = infr.learn_evaluation_verifiers()
>>> edges = list(infr.edges())
>>> verif = verifiers['match_state']
>>> probs = verif.predict_proba_df(edges)
>>> print(probs)
"""
infr.print('learn_evaluataion_verifiers')
from ibeis.algo.verif import vsone
pblm = vsone.OneVsOneProblem(infr, verbose=5)
pblm.primary_task_key = 'match_state'
pblm.eval_clf_keys = ['RF']
pblm.eval_data_keys = ['learn(sum,glob)']
pblm.setup_evaluation()
if True:
pblm.report_evaluation()
verifiers = pblm._make_evaluation_verifiers(pblm.eval_task_keys)
return verifiers
def learn_deploy_verifiers(infr, publish=False):
"""
Uses current knowledge to train verifiers for new unseen pairs.
Example:
>>> import ibeis
>>> ibs = ibeis.opendb('PZ_MTEST')
>>> infr = ibeis.AnnotInference(ibs, aids='all')
>>> infr.ensure_mst()
>>> publish = False
>>> infr.learn_deploy_verifiers()
Ignore:
publish = True
"""
infr.print('learn_deploy_verifiers')
from ibeis.algo.verif import vsone
pblm = vsone.OneVsOneProblem(infr, verbose=True)
pblm.primary_task_key = 'match_state'
pblm.default_clf_key = 'RF'
pblm.default_data_key = 'learn(sum,glob)'
pblm.setup()
dpath = '.'
task_key = 'match_state'
pblm.deploy(dpath, task_key=task_key, publish=publish)
task_key = 'photobomb_state'
if task_key in pblm.eval_task_keys:
pblm.deploy(dpath, task_key=task_key)