def test_stat_scores(pred, target, expected_tp, expected_fp, expected_tn,
expected_fn):
tp, fp, tn, fn = stat_scores(pred, target, class_index=4)
assert tp.item() == expected_tp
assert fp.item() == expected_fp
assert tn.item() == expected_tn
assert fn.item() == expected_fn
def speed_test(self, data):
error = 100
speed_limit = 13.89
speed_thershold = 5.55556
pred = []
y = []
tpr = []
fpr = []
while error >= 0:
pred = []
y = []
for record in data:
d, e = record
pred.append(1 if e > error else 0)
y.append(1 if d - speed_limit > speed_thershold else 0)
# print('Accuracy',
# self.accuracy(tensor(pred, device=device("cuda", 0)), tensor(y, device=device("cuda", 0))).item())
# print('Precision',
# self.Precision(tensor(pred, device=device("cuda", 0)), tensor(y, device=device("cuda", 0))).item())
# print('Recall',
# self.Recall(tensor(pred, device=device("cuda", 0)), tensor(y, device=device("cuda", 0))).item())
# fpr, tpr, thresholds = self.ROC(tensor(pred, device=device("cuda", 0)),
# tensor(y, device=device("cuda", 0)))
tps, fps, tns, fns, sups = stat_scores(
tensor(pred, device=device("cuda", 0)),
tensor(y, device=device("cuda", 0)),
class_index=1)
print(error, tps.item(), fps.item(), tns.item(), fns.item(),
tps.item() / (tps.item() + fns.item()),
fps.item() / (fps.item() + tns.item()))
tpr.append(tps.item() / (tps.item() + fns.item()))
fpr.append(fps.item() / (fps.item() + tns.item()))
error -= 0.25
plt.plot(fpr, tpr)
plt.grid(True)
plt.show()
