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Weekly Report -30/03/14




Once the eventing script had been tested properly, I moved on to including the option of producing a CSV file out of the results.
Afterwards, I wrote a Python script to read in the manually classified events (ground truth) and the results from the eventing script. Then, the entries are compared and matched to produce a list containing the following info: ts event started and severity score(from ground truth), fusion method probabilities (from eventing results, which include DS, bayes, averaging and a newer method which counts the number of detectors that fired and allocates an appropriate severity score), and finally the timestamp at which point each fusion method detected that the event group is significant. This is useful to determine which fusion method performs best (e.g. fastest at detecting significant events, smaller number of FPS(ground truth says not significant, but fusion method detects significance), etc.
The script performs better than I expected after testing (e.g. 46 event groups from the ground truth and 42 matched event group/probability results from the eventing script when tested with Google's stream). The remaining unmatched events will need to be manually sorted out, so hopefully the script will perform as well on AMP data.