def to_json(self, f): perf = {} if self.auc: perf['auc'] = { 'mean': to_percentage(self.auc_mean), 'std': trunc(self.auc_std) } if self.probabilist: perf['thresholds'] = [{} for x in self.thresholds] for t in self.thresholds: summary = self.perf_threshold_summary[t] for v in summary.index: perf['thresholds'][t][v] = {} perf['thresholds'][t][v]['mean'] = to_percentage( summary.loc[v, 'mean']) perf['thresholds'][t][v]['std'] = trunc(summary.loc[v, 'std']) else: for v in self.perf_threshold_summary.index: perf[v] = {} perf[v]['mean'] = to_percentage( self.perf_threshold_summary.loc[v, 'mean']) perf[v]['std'] = trunc(self.perf_threshold_summary.loc[v, 'std']) json.dump(perf, f, indent=2)
def to_json(self, f): perf = {} perf['accuracy'] = { 'mean': to_percentage(self.accuracy_mean), 'std': trunc(self.accuracy_std) } perf['f1_micro'] = { 'mean': to_percentage(self.f1_micro_mean), 'std': trunc(self.f1_micro_std) } perf['f1_macro'] = { 'mean': to_percentage(self.f1_macro_mean), 'std': trunc(self.f1_macro_std) } json.dump(perf, f, indent=2)
def get_exp_name(self): name = '__AlertsAnalysis__' name += 'threshold_%s__' % to_percentage(self.detection_threshold) if self.clustering_conf is not None: name += self.clustering_conf.get_exp_name() elif self.classifier_conf is not None: name += self.classifier_conf.get_exp_name() return name
def get_exp_name(self): return '__Test_TemporalSplit_%s_%s' % ( to_percentage(self.test_size), OneFoldTestConf.get_exp_name(self))
def get_exp_name(self): return '__detectionThreshold_%s__%s' % (to_percentage( self.detection_threshold), self.clustering_conf.get_exp_name())