def debiased_estimator(p, l):
data = list(zip(p, l))
binned_data = cal.bin(data, bins)
if lp == 2:
return cal.unbiased_l2_ce(binned_data)
else:
return cal.normal_debiased_ce(binned_data, power=lp)
def evaluate_l2ce(f, calibrator, z_dist, n):
"""Returns the calibration error of the calibrator on z_dist, f using n samples."""
zs = z_dist(size=n)
ps = f(zs)
phats = calibrator.calibrate(zs)
bins = cal.get_discrete_bins(phats)
data = list(zip(phats, ps))
binned_data = cal.bin(data, bins)
return cal.plugin_ce(binned_data) ** 2
def eval_marginal_calibration(probs, probs, labels, plugin=True):
ces = [] # Compute the calibration error per class, then take the average.
k = probs.shape[1]
labels_one_hot = cal.get_labels_one_hot(np.array(labels), k)
for c in range(k):
probs_c = probs[:, c]
labels_c = labels_one_hot[:, c]
data_c = list(zip(probs_c, labels_c))
bins_c = cal.get_discrete_bins(probs_c)
binned_data_c = cal.bin(data_c, bins_c)
if plugin:
ce_c = cal.plugin_ce(binned_data_c)**2
else:
ce_c = cal.unbiased_square_ce(binned_data_c)
ces.append(ce_c)
return np.mean(ces)
def estimate_ce(data, estimator):
zs = [z for z, y in data]
binned_data = calibration.bin(data, calibration.get_discrete_bins(zs))
return estimator(binned_data)
def main():
# Make synthetic dataset.
np.random.seed(0) # Keep results consistent.
num_points = 1000
(zs, ys) = synthetic_data_1d(num_points=num_points)
# Estimate a lower bound on the calibration error.
# Here z_i is the confidence of the uncalibrated model, y_i is the true label.
# In simple_example.py we used get_calibration_error, but for advanced users
# we recommend using the more explicit lower_bound_scaling_ce to have
# more control over functionality, and be explicit about the semantics -
# that we are only estimating a lower bound.
l2_calibration_error = calibration.lower_bound_scaling_ce(zs, ys)
print("Uncalibrated model l2 calibration error is > %.2f%%" % (100 * l2_calibration_error))
# We can break this down into multiple steps. 1. We choose a binning scheme,
# 2. we bin the data, and 3. we measure the calibration error.
# Each of these steps can be customized, and users can substitute the component
# with their own code.
data = list(zip(zs, ys))
bins = calibration.get_equal_bins(zs, num_bins=10)
l2_calibration_error = calibration.unbiased_l2_ce(calibration.bin(data, bins))
print("Uncalibrated model l2 calibration error is > %.2f%%" % (100 * l2_calibration_error))
# Use Platt binning to train a recalibrator.
calibrator = calibration.PlattBinnerCalibrator(num_points, num_bins=10)
calibrator.train_calibration(np.array(zs), ys)
# Measure the calibration error of recalibrated model.
# In this case we have a binning model, so we can estimate the true calibration error.
# Again, for advanced users we recommend being explicit and using get_binning_ce instead
# of get_calibration_error.
(test_zs, test_ys) = synthetic_data_1d(num_points=num_points)
calibrated_zs = list(calibrator.calibrate(test_zs))
l2_calibration_error = calibration.get_binning_ce(calibrated_zs, test_ys)
print("Scaling-binning l2 calibration error is %.2f%%" % (100 * l2_calibration_error))
# As above we can break this down into 3 steps. Notice here we have a binning model,
# so we use get_discrete_bins to get all the bins (all possible values the model
# outputs).
data = list(zip(calibrated_zs, test_ys))
bins = calibration.get_discrete_bins(calibrated_zs)
binned = calibration.bin(data, bins)
l2_calibration_error = calibration.unbiased_l2_ce(calibration.bin(data, bins))
print("Scaling-binning l2 calibration error is %.2f%%" % (100 * l2_calibration_error))
# Compute calibration error and confidence interval.
# In the simple_example.py we just called get_calibration_error_uncertainties.
# This function uses the bootstrap to estimate confidence intervals.
# The bootstrap first requires us to define the functional we are trying to
# estimate, and then resamples the data multiple times to estimate confidence intervals.
def estimate_ce(data, estimator):
zs = [z for z, y in data]
binned_data = calibration.bin(data, calibration.get_discrete_bins(zs))
return estimator(binned_data)
functional = lambda data: estimate_ce(data, lambda x: calibration.plugin_ce(x))
[lower, _, upper] = calibration.bootstrap_uncertainty(data, functional, num_samples=100)
print(" Confidence interval is [%.2f%%, %.2f%%]" % (100 * lower, 100 * upper))
# Advanced: boostrap can be used to debias the l1-calibration error (ECE) as well.
# This is a heuristic, which does not (yet) come with a formal guarantee.
functional = lambda data: estimate_ce(data, lambda x: calibration.plugin_ce(x, power=1))
[lower, mid, upper] = calibration.bootstrap_uncertainty(data, functional, num_samples=100)
print("Debiased estimate of L1 calibration error is %.2f%%" % (100 * mid))
print(" Confidence interval is [%.2f%%, %.2f%%]" % (100 * lower, 100 * upper))
def estimator(data):
binned_data = cal.bin(data, bins)
return cal.plugin_ce(binned_data, power=lp)
def plugin_estimator(p, l):
data = list(zip(p, l))
binned_data = cal.bin(data, bins)
return cal.plugin_ce(binned_data, power=lp)
def eval_top_calibration(probs, probs, labels):
correct = (cal.get_top_predictions(probs) == labels)
data = list(zip(probs, correct))
bins = cal.get_discrete_bins(probs)
binned_data = cal.bin(data, bins)
return cal.plugin_ce(binned_data)**2
def estimate_unbiased_ce(data):
binned_data = cal.bin(data, bins)
return np.sqrt(max(0.0, cal.unbiased_square_ce(binned_data)))