Created
May 28, 2020 17:43
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import pandas as pd | |
import numpy as np | |
def calibration_error(y_true, y_prob, n_bins=5, strategy='uniform', return_expected_caliberation_error=True): | |
if strategy == 'quantile': # Determine bin edges by distribution of data | |
quantiles = np.linspace(0, 1, n_bins + 1) | |
bins = np.percentile(y_prob, quantiles * 100) | |
bins[-1] = bins[-1] + 1e-8 | |
elif strategy == 'uniform': | |
bins = np.linspace(0., 1. + 1e-8, n_bins + 1) | |
else: | |
raise ValueError("Invalid entry to 'strategy' input. Strategy " | |
"must be either 'quantile' or 'uniform'.") | |
y_prob_max = np.max(y_prob, axis=-1) | |
binids = np.digitize(y_prob_max, bins) - 1 | |
y_correct_classified = (np.argmax(y_true, axis=-1) == np.argmax(y_prob, axis=-1)).astype(int) | |
bin_sums = np.bincount(binids, weights=y_prob_max, minlength=len(bins)) | |
bin_true = np.bincount(binids, weights=y_correct_classified, minlength=len(bins)) | |
bin_total = np.bincount(binids, minlength=len(bins)) | |
nonzero = bin_total != 0 | |
# acc(Bm) | |
prob_true = bin_true[nonzero] / bin_total[nonzero] | |
#conf(Bm) | |
prob_pred = bin_sums[nonzero] / bin_total[nonzero] | |
expected_caliberation_error = np.sum(bin_total[nonzero] * np.abs(prob_true - prob_pred))/bin_total[nonzero].sum() | |
overconfidence_error = np.sum(bin_total[nonzero] * prob_pred * np.max(np.concatenate(((prob_pred - prob_true).reshape(-1, 1), np.zeros((1, len(prob_pred))).T), axis=1), axis=-1)/bin_total[nonzero].sum()) | |
return prob_true, prob_pred, expected_caliberation_error, overconfidence_error |
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