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())