made stuff a bit more userfriendly
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main.py
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main.py
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#data loading
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import numpy as np
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from stroo import *
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f=np.load("useragents.npz")
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model=train_model(f["train"],n=2)
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from stroo import train_model
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#understands pure strings
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#definitely not the best possible model
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#so if this is at least >0.55 (on more complicated data) we should be able to do something with it
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#also migth be included into an isolation forest
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model=train_model(f["train"],n=3)
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#calculate auc. Has disparity between normal, abnormal: np.mean(testy)~=0.065
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print(model.eval(f["testx"],f["testy"]))
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#in my tests reaches >0.99
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numpy
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keras
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tqdm
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sklearn
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