ytrue = Y[idx]
lens = inputs[1].any(2).sum(1)
avg_ones = np.hstack([Y[k,:l,0] for k,l in zip(idx, lens)]).mean()
if avg_ones > 0:
w = c*1/avg_ones
net.logit_loss_fn = nn.CrossEntropyLoss(reduce = False,
weight = torch.FloatTensor([1, w]))
net.count_loss_fn = nn.CrossEntropyLoss(reduce = False,
weight = torch.FloatTensor([1, c2*1/avg_ones]))
lloss, closs, ylogits, ycount = net.run(*inputs, ytrue, lens)
ctrue = ytrue.any(1)
ltp, ltn, lfp, lfn = confusion(ylogits, ytrue, lens)
ctp, ctn, cfp, cfn = confusion1d(ycount, ytrue.any(1).flatten())
ltpr = ltp/(ltp+lfn)
ltnr = ltn/(ltn+lfp)
lacc = (ltp + ltn)/(ltp+lfp+ltn+lfn)
ctpr = ctp/(ctp+cfn)
ctnr = ctn/(ctn+cfp)
cacc = (ctp + ctn)/(ctp+cfp+ctn+cfn)
if ltpr < .1 and ltnr > .5:
c = np.minimum(c*1.001, 5)
elif ltnr < .1 and ltpr > .5:
c = np.maximum(.999*c, .1)
if ctpr < .1 and ctnr > .5:
c2 = np.minimum(c2*1.01, 5)
idx = np.random.choice(n, size=batch_size)
inputs = (A[idx], Z[idx])
ytrue = Y[idx]
lens = inputs[1].any(2).sum(1)
avg_ones = np.hstack([Y[k, :l, 0] for k, l in zip(idx, lens)]).mean()
if avg_ones > 0:
w = c * 1 / avg_ones
net.logit_loss_fn = nn.CrossEntropyLoss(reduce=True,
weight=torch.FloatTensor(
[1, w]))
lloss, closs, ylogits, ycount = net.run(*inputs, ytrue)
cacc = np.mean(ycount.round() == ytrue.sum(1))
tp, tn, fp, fn = confusion(ylogits, ytrue, lens)
tpr = tp / (tp + fn)
tnr = tn / (tn + fp)
lacc = (tp + tn) / (tp + fp + tn + fn)
if tpr < .1:
c = np.minimum(c * 1.001, 5)
if tnr < .1:
c = np.maximum(.999 * c, .1)
msg = "{:1.4f},{:1.4f},{:1.4f},"\
"{:1.4f},{:1.4f},{:1.4f},{:1.4f}"\
.format(lloss,
closs,
tpr, # True positive rate
tnr, # True negative rate