autointent.metrics.scoring.scoring_log_likelihood#

autointent.metrics.scoring.scoring_log_likelihood(labels, scores, eps=1e-10)#

Calculate log likelihood score for 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 the i-th utterance having the ground truth label.

Multilabel case: Mean negative binary cross-entropy:

$$\frac{1}{\ell }\sum _{i=1}^{\ell }\sum _{c=1}^C[y[i,c]\cdot \log (s[i,c])+(1-y[i,c])\cdot \log (1-s[i,c])]$$

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.

Raises:

ValueError – If any scores are not in the range (0,1].

Return type:

float