Machine Learning Toolkit - Density Function Hello, I'm trying to use the machine learning tool in order to create a model based on a time frame and then analyze the current time window in order to see if it stands out from an average behavior fort his hour of the day on this day of the week. When trying to build the model with a command like this:   index=onlineservices "USERACTION: Successful login" earliest=-33d@d latest=-1d@d | bin _time span=15m | eval date_minutebin=strftime(_time, "%M") | eval date_hour=strftime(_time, "%H") | eval date_wday=strftime(_time, "%A") | stats count by _time date_minutebin date_hour date_wday  | fit DensityFunction count by "date_minutebin,date_hour,date_wday" into mydensitymodel threshold=0.5 dist=norm sample=True   I get the following error: Error in 'fit' command: Error while fitting "DensityFunction" model: 'module' object has no attribute 'wasserstein_distance'   I have also tryed to change the metrics to kolmogorov_smirnov, but I get another error in this case: Error in 'fit' command: Error while fitting "DensityFunction" model: 'functools.partial' object has no attribute '__module__'   Can someone help me with this? Why the default metrics does not work in my case?   Bellow you ca find an example of the data I get from the first search, before piping into FIT function:   _time    date_minutebin              date_hour          date_wday         count 2021-04-29 00:00:00       00           00           Thursday             4 2021-04-29 00:15:00       15           00           Thursday             3 2021-04-29 00:30:00       30           00           Thursday             2 2021-04-29 00:45:00       45           00           Thursday             3 2021-04-29 01:00:00       00           01           Thursday             2 2021-04-29 01:15:00       15           01           Thursday             1 2021-04-29 01:45:00       45           01           Thursday             2 2021-04-29 02:00:00       00           02           Thursday             3 2021-04-29 02:15:00       15           02           Thursday             1 2021-04-29 02:30:00       30           02           Thursday             2 2021-04-29 02:45:00       45           02           Thursday             1 2021-04-29 03:00:00       00           03           Thursday             1 2021-04-29 03:15:00       15           03           Thursday             2 2021-04-29 03:30:00       30           03           Thursday             1 2021-04-29 03:45:00       45           03           Thursday             2 2021-04-29 04:00:00       00           04           Thursday             1 2021-04-29 04:15:00       15           04           Thursday             2 2021-04-29 04:30:00       30           04           Thursday             1 ... View more