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f1.py
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Fri, Apr 19, 13:42

f1.py
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import torch
import sys
def reverse(t):
idx = [i for i in range(t.size(0)-1, -1, -1)]
idx = torch.LongTensor(idx)
it = t.index_select(0, idx)
return it
def PRFromHistograms(hPos,hNeg):
#print("hPos",hPos,"hNeg",hNeg)
positives=hPos.sum()
negatives=hNeg.sum()
#print("positives, negatives", positives, negatives)
truepositives =reverse(hPos.clone()).long().cumsum(dim=0)
falsepositives=reverse(hNeg.clone()).long().cumsum(dim=0)
predpositives=torch.add(truepositives,falsepositives)
#protect against zero division
predpositives[predpositives==0]=1
precision=torch.Tensor.div_(truepositives.float(),predpositives.float())
recall =torch.Tensor.div (truepositives.float(),positives.float())
precision[precision<=0]=sys.float_info.epsilon
recall [recall <=0]=sys.float_info.epsilon
#print("precision,recall",precision,recall)
return precision,recall
def PRFromDiscrete(nTruePos,nPredPos,nGtPos):
truepositives =nTruePos
falsepositives=nPredPos-nTruePos
positives =nGtPos
predpositives =nPredPos
if predpositives==0: predpositives =1 #protect against zero division
precision=truepositives/float(predpositives)
recall =truepositives/float(positives)
if precision<=0: precision=sys.float_info.epsilon
if recall <=0: recall =sys.float_info.epsilon
return precision,recall
def PRFromOutGt(outps,targs,nbins=10000):
hPos=torch.zeros(nbins)
hNeg=torch.zeros(nbins)
for o,t in zip(outps,targs):
pos=o[lbl==1]
neg=t[lbl==0]
hPos+=torch.from_numpy(pos.astype(np.float32)).histc(nbins,0,1)
hNeg+=torch.from_numpy(neg.astype(np.float32)).histc(nbins,0,1)
precision,recall=f1.PRFromHistograms(hPos,hNeg)
f1s=f1.F1FromPR(precision,recall)
f=f1s.max()
return f
def F1FromPR(p,r):
suminv=torch.pow(p,-1)+torch.pow(r,-1)
f1s=torch.pow(suminv,-1).mul(2)
#print("f1s",f1s)
return f1s

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