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