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Fix: change evaluation dictionary keys #66

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Merged
sarusso merged 4 commits into from
Dec 10, 2025
Merged

Fix: change evaluation dictionary keys #66

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105e54f
Change the key 'anomalies_count' of the evaluation result in 'true_po…
agataben Dec 7, 2025
beec504
Change the key 'anomalies_ratio' of the evaluation result in 'true_po…
agataben Dec 7, 2025
4804720
Change the key '_anomalies_ratio' of the breakdown info in '_true_pos…
agataben Dec 7, 2025
06bc317
Change the key '_anomalies_count' of the breakdown info in '_true_pos…
agataben Dec 7, 2025
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50 changes: 25 additions & 25 deletions ats/evaluators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,28 +54,28 @@ def _calculate_model_scores(single_model_evaluation={}):
false_positives_ratio = 0
anomalous_series_n = 0
for sample in single_model_evaluation.keys():
anomalies_count += single_model_evaluation[sample]['anomalies_count']
if single_model_evaluation[sample]['anomalies_ratio'] is not None:
anomalies_ratio += single_model_evaluation[sample]['anomalies_ratio']
anomalies_count += single_model_evaluation[sample]['true_positives_count']
if single_model_evaluation[sample]['true_positives_rate'] is not None:
anomalies_ratio += single_model_evaluation[sample]['true_positives_rate']
anomalous_series_n += 1
false_positives_count += single_model_evaluation[sample]['false_positives_count']
false_positives_ratio += single_model_evaluation[sample]['false_positives_ratio']

model_scores['anomalies_count'] = anomalies_count
model_scores['true_positives_count'] = anomalies_count
if anomalous_series_n:
model_scores['anomalies_ratio'] = anomalies_ratio/anomalous_series_n
model_scores['true_positives_rate'] = anomalies_ratio/anomalous_series_n
else:
model_scores['anomalies_ratio'] = None
model_scores['true_positives_rate'] = None
model_scores['false_positives_count'] = false_positives_count
model_scores['false_positives_ratio'] = false_positives_ratio/len(single_model_evaluation)
return model_scores

def _get_breakdown_info(single_model_evaluation={}):
for sample in single_model_evaluation.keys():
if 'anomalies_count' in single_model_evaluation[sample].keys():
del single_model_evaluation[sample]['anomalies_count']
if 'anomalies_ratio' in single_model_evaluation[sample].keys():
del single_model_evaluation[sample]['anomalies_ratio']
if 'true_positives_count' in single_model_evaluation[sample].keys():
del single_model_evaluation[sample]['true_positives_count']
if 'true_positives_rate' in single_model_evaluation[sample].keys():
del single_model_evaluation[sample]['true_positives_rate']
if 'false_positives_count' in single_model_evaluation[sample].keys():
del single_model_evaluation[sample]['false_positives_count']
if 'false_positives_ratio' in single_model_evaluation[sample].keys():
Expand All @@ -94,7 +94,7 @@ def _get_breakdown_info(single_model_evaluation={}):
breakdown_info[key] = sample_evaluation[key]

for key in breakdown_info.keys():
if '_ratio' in key:
if '_rate' in key:
breakdown_info[key] /= anomaly_series_count_by_type[key]

return breakdown_info
Expand Down Expand Up @@ -196,17 +196,17 @@ def _variable_granularity_evaluation(flagged_timeseries_df,anomaly_labels_df,bre
false_positives_count += flagged_timeseries_df.loc[timestamp,column]
if anomaly is not None:
total_detected_anomalies_n += anomaly_count
breakdown_info[anomaly + '_anomaly' + '_count'] = anomaly_count
breakdown_info[anomaly + '_anomaly' + '_ratio'] = anomaly_count/(frequency * variables_n)
breakdown_info[anomaly + '_true_positives_count'] = anomaly_count
breakdown_info[anomaly + '_true_positives_rate'] = anomaly_count/(frequency * variables_n)

total_inserted_anomalies_n *= variables_n
one_series_evaluation_result['false_positives_count'] = false_positives_count
one_series_evaluation_result['false_positives_ratio'] = false_positives_count/normalization_factor
one_series_evaluation_result['anomalies_count'] = total_detected_anomalies_n
one_series_evaluation_result['true_positives_count'] = total_detected_anomalies_n
if total_inserted_anomalies_n:
one_series_evaluation_result['anomalies_ratio'] = total_detected_anomalies_n/total_inserted_anomalies_n
one_series_evaluation_result['true_positives_rate'] = total_detected_anomalies_n/total_inserted_anomalies_n
else:
one_series_evaluation_result['anomalies_ratio'] = None
one_series_evaluation_result['true_positives_rate'] = None
if breakdown:
return one_series_evaluation_result | breakdown_info
else:
Expand Down Expand Up @@ -235,16 +235,16 @@ def _point_granularity_evaluation(flagged_timeseries_df,anomaly_labels_df,breakd
break
if anomaly is not None:
total_detected_anomalies_n += anomaly_count
breakdown_info[anomaly + '_anomaly_count'] = anomaly_count
breakdown_info[anomaly + '_anomaly_ratio'] = anomaly_count/frequency
breakdown_info[anomaly + '_true_positives_count'] = anomaly_count
breakdown_info[anomaly + '_true_positives_rate'] = anomaly_count/frequency

one_series_evaluation_result['false_positives_count'] = false_positives_count
one_series_evaluation_result['false_positives_ratio'] = false_positives_count/normalization_factor
one_series_evaluation_result['anomalies_count'] = total_detected_anomalies_n
one_series_evaluation_result['true_positives_count'] = total_detected_anomalies_n
if total_inserted_anomalies_n:
one_series_evaluation_result['anomalies_ratio'] = total_detected_anomalies_n/total_inserted_anomalies_n
one_series_evaluation_result['true_positives_rate'] = total_detected_anomalies_n/total_inserted_anomalies_n
else:
one_series_evaluation_result['anomalies_ratio'] = None
one_series_evaluation_result['true_positives_rate'] = None
if breakdown:
return one_series_evaluation_result | breakdown_info
else:
Expand All @@ -267,13 +267,13 @@ def _series_granularity_evaluation(flagged_timeseries_df,anomaly_labels_df,break
is_series_anomalous = 1
if anomalies:
inserted_anomaly = anomalies[0]
breakdown_info[inserted_anomaly + '_anomaly_count'] = 1
breakdown_info[inserted_anomaly + '_anomaly_ratio'] = 1
breakdown_info[inserted_anomaly + '_true_positives_count'] = 1
breakdown_info[inserted_anomaly + '_true_positives_rate'] = 1
break
one_series_evaluation_result['false_positives_count'] = 1 if is_series_anomalous and not anomalies else 0
one_series_evaluation_result['false_positives_ratio'] = one_series_evaluation_result['false_positives_count']
one_series_evaluation_result['anomalies_count'] = 1 if is_series_anomalous and anomalies else 0
one_series_evaluation_result['anomalies_ratio'] = one_series_evaluation_result['anomalies_count'] if anomalies else None
one_series_evaluation_result['true_positives_count'] = 1 if is_series_anomalous and anomalies else 0
one_series_evaluation_result['true_positives_rate'] = one_series_evaluation_result['true_positives_count'] if anomalies else None

if breakdown:
return one_series_evaluation_result | breakdown_info
Expand Down
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