def train_calibration(self, logits, labels):
"""Train a calibrator given logits and labels.
Args:
logits: A sequence of dimension (n, k) where n is the number of
data points, and k is the number of classes, representing
the output probabilities/confidences of the uncalibrated
model.
labels: A sequence of length n, where n is the number of data points,
representing the ground truth label for each data point.
"""
assert(len(logits) >= self._num_calibration)
logits = np.array(logits)
self._k = logits.shape[1] # Number of classes.
assert self._k == np.max(labels) - np.min(labels) + 1
labels_one_hot = utils.get_labels_one_hot(np.array(labels), self._k)
self._calibrators = []
for c in range(self._k):
# For each class c, get the probabilities the model output for that class, and whether
# the data point was actually class c, or not.
probs_c = logits[:, c]
labels_c = labels_one_hot[:, c]
bins = utils.get_equal_bins(probs_c, num_bins=self._num_bins)
calibrator_c = utils.get_histogram_calibrator(probs_c, labels_c, bins)
self._calibrators.append(calibrator_c)
def test_binning(B, N, T): num_well_balanced = 0 for i in range(T): samples = np.random.uniform(size=N) bins = utils.get_equal_bins(samples, num_bins=B) num_well_balanced += well_balanced(bins, 2.0) return 1.0 * num_well_balanced / T
def train_calibration(self, logits, labels):
assert(len(logits) >= self._num_calibration)
probs = utils.get_top_probs(logits)
predictions = utils.get_top_predictions(logits)
correct = (predictions == labels)
bins = utils.get_equal_bins(probs, num_bins=self._num_bins)
self._calibrator = utils.get_histogram_calibrator(
probs, correct, bins)
def train_calibration(self, logits, labels):
assert(len(logits) >= self._num_calibration)
predictions = utils.get_top_predictions(logits)
probs = utils.get_top_probs(logits)
correct = (predictions == labels)
self._platt = utils.get_platt_scaler(
probs, correct)
platt_probs = self._platt(probs)
bins = utils.get_equal_bins(platt_probs, num_bins=self._num_bins)
self._discrete_calibrator = utils.get_discrete_calibrator(
platt_probs, bins)
def compare_estimators(logits,
labels,
platt_data_size,
bin_data_size,
num_bins,
ver_base_size=2000,
ver_size_increment=1000,
num_resamples=100,
save_name='cmp_est'):
# Convert logits to prediction, probs.
predictions = utils.get_top_predictions(logits)
probs = utils.get_top_probs(logits)
correct = (predictions == labels)
# Platt scale on first chunk of data
platt = utils.get_platt_scaler(probs[:platt_data_size],
correct[:platt_data_size])
platt_probs = platt(probs)
estimator_names = ['biased', 'unbiased']
estimators = [
lambda x: utils.plugin_ce(x)**2, utils.improved_unbiased_square_ce
]
bins = utils.get_equal_bins(platt_probs[:platt_data_size + bin_data_size],
num_bins=num_bins)
binner = utils.get_discrete_calibrator(
platt_probs[platt_data_size:platt_data_size + bin_data_size], bins)
verification_probs = binner(platt_probs[platt_data_size + bin_data_size:])
verification_correct = correct[platt_data_size + bin_data_size:]
verification_data = list(zip(verification_probs, verification_correct))
verification_sizes = list(
range(ver_base_size,
len(verification_probs) + 1, ver_size_increment))
# We want to compare the two estimators when varying the number of samples.
# However, a single point of comparison does not tell us much about the estimators.
# So we use resampling - we resample from the test set many times, and run the estimators
# on the resamples. We stores these values. This gives us a sense of the range of values
# the estimator might output.
# So estimates[i][j][k] stores the estimate when using estimator i, with verification_sizes[j]
# samples, in the k-th resampling.
estimates = np.zeros(
(len(estimators), len(verification_sizes), num_resamples))
# We also store the certified estimates. These represent the upper bounds we get using
# each estimator. They are computing using the std-dev of the estimator estimated by
# Bootstrap.
cert_estimates = np.zeros(
(len(estimators), len(verification_sizes), num_resamples))
for ver_idx, verification_size in zip(range(len(verification_sizes)),
verification_sizes):
for k in range(num_resamples):
# Resample
indices = np.random.choice(list(range(len(verification_data))),
size=verification_size,
replace=True)
cur_verification_data = [verification_data[i] for i in indices]
cur_verification_probs = [verification_probs[i] for i in indices]
bins = utils.get_discrete_bins(cur_verification_probs)
# Compute estimates for each estimator.
for i in range(len(estimators)):
def estimator(data):
binned_data = utils.bin(data, bins)
return estimators[i](binned_data)
cur_estimate = estimator(cur_verification_data)
estimates[i][ver_idx][k] = cur_estimate
# cert_resampling_estimates[j].append(
# cur_estimate + utils.bootstrap_std(cur_verification_data, estimator, num_samples=20))
estimates = np.sort(estimates, axis=-1)
lower_bound = int(0.1 * num_resamples)
median = int(0.5 * num_resamples)
upper_bound = int(0.9 * num_resamples)
lower_estimates = estimates[:, :, lower_bound]
upper_estimates = estimates[:, :, upper_bound]
median_estimates = estimates[:, :, median]
# We can also compute the MSEs of the estimator.
bins = utils.get_discrete_bins(verification_probs)
true_calibration = utils.plugin_ce(utils.bin(verification_data, bins))**2
print(true_calibration)
print(np.sqrt(np.mean(estimates[1, -1, :])))
errors = np.abs(estimates - true_calibration)
accumulated_errors = np.mean(errors, axis=-1)
error_bars_90 = 1.645 * np.std(errors, axis=-1) / np.sqrt(num_resamples)
print(accumulated_errors)
plt.errorbar(verification_sizes,
accumulated_errors[0],
yerr=[error_bars_90[0], error_bars_90[0]],
barsabove=True,
color='red',
capsize=4,
label='plugin')
plt.errorbar(verification_sizes,
accumulated_errors[1],
yerr=[error_bars_90[1], error_bars_90[1]],
barsabove=True,
color='blue',
capsize=4,
label='debiased')
plt.ylabel("Mean-Squared-Error")
plt.xlabel("Number of Samples")
plt.legend(loc='upper right')
plt.show()
plt.ylabel("Number of estimates")
plt.xlabel("Absolute deviation from ground truth")
bins = np.linspace(np.min(errors[:, 0, :]), np.max(errors[:, 0, :]), 40)
plt.hist(errors[0][0], bins, alpha=0.5, label='plugin')
plt.hist(errors[1][0], bins, alpha=0.5, label='debiased')
plt.legend(loc='upper right')
plt.gca().yaxis.set_major_formatter(PercentFormatter(xmax=num_resamples))
plt.show()
def train_calibration(self, zs, ys):
self._platt = utils.get_platt_scaler(zs, ys)
platt_probs = self._platt(zs)
bins = utils.get_equal_bins(platt_probs, num_bins=self._num_bins)
self._discrete_calibrator = utils.get_discrete_calibrator(platt_probs, bins)
def train_calibration(self, zs, ys):
bins = utils.get_equal_bins(zs, num_bins=self._num_bins)
self._calibrator = utils.get_histogram_calibrator(zs, ys, bins)