I just came across an interesting corner case: some bins have samples of the same probability.
The code below will reproduce the error.
import calibration as cal
model_probs = [[0.5507, 0.4493],
[0.8764, 0.1236],
[0.1822, 0.8178],
[0.3814, 0.6186],
[0.9725, 0.0275],
[0.281, 0.719 ],
[0.8817, 0.1183],
[0.8193, 0.1807],
[0.4806, 0.5194],
[0.9415, 0.0585],
[0.4648, 0.5352],
[0.9561, 0.0439]]
labels = [0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0]
calibrator = cal.PlattBinnerMarginalCalibrator(len(labels), num_bins=4)
calibrator.train_calibration(model_probs, labels)
print (calibrator._bins)
The shape of the first row in calibrator._bins is (3,) instead of (4,) as expected.
We looked into the reason and found that the last two bins have samples of the same probabilities.
We are wondering whether in such a case, an error message should be thrown out or the probabilities should have been added with noises.
Hi,
I just came across an interesting corner case: some bins have samples of the same probability.
The code below will reproduce the error.
The shape of the first row in calibrator._bins is (3,) instead of (4,) as expected.
We looked into the reason and found that the last two bins have samples of the same probabilities.
We are wondering whether in such a case, an error message should be thrown out or the probabilities should have been added with noises.