def load_supervised(self):
self.combiner, self.dh = ibcc.load_combiner(self.configfile)
self.nclasses = self.combiner.nclasses
if self.dh.goldsubtypes != None and len(self.dh.goldsubtypes)>0 and self.nclasses>2:
self.discretize_secondary_gold()
self.gold_tr = np.zeros(len(self.dh.goldlabels)) -1
self.gold_tr[self.dh.trainids] = self.disc_gold_types[self.dh.trainids]
elif self.dh.trainids != None:
self.gold_tr = np.zeros(len(self.dh.goldlabels)) -1
self.gold_tr[self.dh.trainids] = self.dh.goldlabels[self.dh.trainids]
else:
self.gold_tr = self.dh.goldlabels
def test_unsupervised(self, evaluate=True):
# no training data, test all points we have true labels for
self.combiner, self.dh = ibcc.load_combiner(self.configfile)
self.gold_tr = np.zeros(len(self.dh.goldlabels)) -1
self.nclasses = self.combiner.nclasses
if evaluate:
acc,recall,spec,prec,auc,ap,nfiltered,filter_rate \
= self.run_test(evaluate=evaluate)
self.print_results(acc,recall,spec,prec,auc,ap,nfiltered,filter_rate)
else:
acc,recall,spec,prec,auc,ap,nfiltered,filter_rate = self.run_test(evaluate=evaluate)
result_array = self.make_result_list(acc, recall, spec, prec, auc, ap, nfiltered, filter_rate)
return result_array
def load_supervised(self):
self.combiner, self.dh = ibcc.load_combiner(self.configfile)
self.nclasses = self.combiner.nclasses
if self.dh.goldsubtypes != None and len(
self.dh.goldsubtypes) > 0 and self.nclasses > 2:
self.discretize_secondary_gold()
self.gold_tr = np.zeros(len(self.dh.goldlabels)) - 1
self.gold_tr[self.dh.trainids] = self.disc_gold_types[
self.dh.trainids]
elif self.dh.trainids != None:
self.gold_tr = np.zeros(len(self.dh.goldlabels)) - 1
self.gold_tr[self.dh.trainids] = self.dh.goldlabels[
self.dh.trainids]
else:
self.gold_tr = self.dh.goldlabels
def test_unsupervised(self, evaluate=True):
# no training data, test all points we have true labels for
self.combiner, self.dh = ibcc.load_combiner(self.configfile)
self.gold_tr = np.zeros(len(self.dh.goldlabels)) - 1
self.nclasses = self.combiner.nclasses
if evaluate:
acc,recall,spec,prec,auc,ap,nfiltered,filter_rate \
= self.run_test(evaluate=evaluate)
self.print_results(acc, recall, spec, prec, auc, ap, nfiltered,
filter_rate)
else:
acc, recall, spec, prec, auc, ap, nfiltered, filter_rate = self.run_test(
evaluate=evaluate)
result_array = self.make_result_list(acc, recall, spec, prec, auc, ap,
nfiltered, filter_rate)
return result_array
