Python 3 implementation of the affiliation metrics and tests for reproducing the experiments described in Local Evaluation of Time Series Anomaly Detection Algorithms, accepted in KDD 2022 Research Track: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Type pip install . to install the affiliation
package. Only the standard Python library is needed, there is no dependency to external libraries.
In a Python session, the following lines give an example for computing the affiliation metrics from prediction and ground truth vectors:
from affiliation.generics import convert_vector_to_events
from affiliation.metrics import pr_from_events
vector_pred = [0, 0, 0, 0, 1, 0, 0, 0, 1, 0]
vector_gt = [0, 0, 0, 1, 0, 0, 0, 1, 1, 1]
events_pred = convert_vector_to_events(vector_pred) # [(4, 5), (8, 9)]
events_gt = convert_vector_to_events(vector_gt) # [(3, 4), (7, 10)]
Trange = (0, len(vector_pred))
pr_from_events(events_pred, events_gt, Trange)
which gives as output:
{'precision': 0.82,
'recall': 0.84,
'individual_precision_probabilities': [0.63, 1.0],
'individual_recall_probabilities': [0.82, 0.87],
'individual_precision_distances': [0.5, 0.0],
'individual_recall_distances': [0.5, 0.33]}
The unit tests can be run by typing:
python -m unittest discover
The results from the paper are also tested.
The specific tests of the results are located at tests/test_data.py and tested
against data located in the folder data/.