Interpreting DonutPredictor.get_score values that are all negative

[useruq]

Hi Haowen,

My data set has continuous points , where each point repesent a day, and not minutes that you have shown in the paper/ sample_data.

I had provided a list of 240 points with a window size of 120 for final evaluation after training for 1000 points. After calling the DonutPredictor.get_score function on these set of points, I am getting the final list of 121 scores - where all scores are negative numbers. How do I interpret the anomaly part here?

You had mentioned in the codes :
The larger reconstruction probability, the less likely a point
is anomaly. You may take the negative of the score, if you want
something to directly indicate the severity of anomaly.

assume there are only 2 scores : -2.3, -0.5,

1. If I keep negative as such , -0.5 is the largest , and -2.3 is the smallest. Then the -2.3 is an anomaly whereas -0.5 is not an anomaly
2. If I take Absolute values of these negative values, then 2.3 is the largest and 0.5 is the smallest. Then 0.5 is an anomaly and 2.3 is not an anomaly

So please help me interpret the results

[lizeyan]

So, here is the only rule: the larger reconstruction probability, the less likely a point
is an anomaly. You should not use absolute values.
Reconstruction probability may be positive or negative since it is the logarithm of the probability density and it can be ang real number actually.