danieltan07 / dagmm

My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

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Model without training works well?

bqdqj opened this issue · comments

commented

Out of curiosity I just tried the test mode directly on the KDD data.
I tried to modify the threshold.
It turns out that the f1 score seems to be quite high even without training.
I didn't load any pretrained model and I set the config to test mode
Does anyone know why?

Threshold for percentile (100 - 10): 14.92182731628418
Accuracy : 0.8065, Precision : 0.9339, Recall : 0.4433, F-score : 0.6013
Threshold for percentile (100 - 12): 11.708395004272461
Accuracy : 0.8364, Precision : 0.9388, Recall : 0.5378, F-score : 0.6838
Threshold for percentile (100 - 14): 8.14496498107912
Accuracy : 0.8659, Precision : 0.9419, Recall : 0.6313, F-score : 0.7559
Threshold for percentile (100 - 16): 3.5739990234374943
Accuracy : 0.8905, Precision : 0.9397, Recall : 0.7130, F-score : 0.8108
Threshold for percentile (100 - 18): 1.8419023990631087
Accuracy : 0.9084, Precision : 0.9210, Recall : 0.7894, F-score : 0.8501
Threshold for percentile (100 - 20): 1.555919885635376
Accuracy : 0.9339, Precision : 0.9169, Recall : 0.8786, F-score : 0.8974
Best Threshold : 1.555919885635376, with Accuracy : 0.9339, Precision : 0.9169, Recall : 0.8786, F-score : 0.8974