autointent.metrics.scoring_log_likelihood#
- autointent.metrics.scoring_log_likelihood(labels, scores, eps=1e-10)#
Supports multiclass and multilabel cases.
Multiclass case: Mean negative cross-entropy for each utterance classification result:
\[\frac{1}{\ell}\sum_{i=1}^{\ell}\log(s[y[i]])\]where
s[y[i]]is the predicted score of thei-th utterance having the ground truth label.Multilabel case: Mean negative binary cross-entropy:
\[\frac{1}{\ell}\sum_{i=1}^\ell\sum_{c=1}^C\Big[y[i,c]\cdot\log(s[i,c])+(1-y[i,c])\cdot\log(1-s[i,c])\Big]\]where
s[i,c]is the predicted score of thei-th utterance having the ground truth labelc.- Parameters:
labels (autointent.metrics.custom_types.LABELS_VALUE_TYPE) – Ground truth labels for each utterance.
scores (autointent.metrics.custom_types.SCORES_VALUE_TYPE) – For each utterance, a list containing scores for each of n_classes classes.
eps (float) – A small value to avoid division by zero.
- Returns:
Score of the scoring metric.
- Return type: