metrics

Different Metric functions.

deeppavlov.metrics.accuracy.sets_accuracy(y_true: [<class 'list'>, <class 'numpy.ndarray'>], y_predicted: [<class 'list'>, <class 'numpy.ndarray'>]) → float

Calculate accuracy in terms of sets coincidence

Parameters

• y_true – true values

• y_predicted – predicted values

Returns

portion of samples with absolutely coincidental sets of predicted values

deeppavlov.metrics.fmeasure.round_f1(y_true, y_predicted)

Calculates F1 (binary) measure.

Parameters

• y_true – list of true values

• y_predicted – list of predicted values

Returns

F1 score

deeppavlov.metrics.fmeasure.round_f1_macro(y_true, y_predicted)

Calculates F1 macro measure.

Parameters

• y_true – list of true values

• y_predicted – list of predicted values

Returns

F1 score

deeppavlov.metrics.fmeasure.round_f1_weighted(y_true, y_predicted)

Calculates F1 weighted measure.

Parameters

• y_true – list of true values

• y_predicted – list of predicted values

Returns

F1 score

deeppavlov.metrics.log_loss.sk_log_loss(y_true: Union[List[List[float]], List[List[int]], numpy.ndarray], y_predicted: Union[List[List[float]], List[List[int]], numpy.ndarray]) → float

Calculates log loss.

Parameters

• y_true – list or array of true values

• y_predicted – list or array of predicted values

Returns

Log loss

deeppavlov.metrics.roc_auc_score.roc_auc_score(y_true: Union[List[List[float]], List[List[int]], numpy.ndarray], y_pred: Union[List[List[float]], List[List[int]], numpy.ndarray]) → float

Compute Area Under the Curve (AUC) from prediction scores.

Parameters

• y_true – true binary labels

• y_pred – target scores, can either be probability estimates of the positive class

Returns

Area Under the Curve (AUC) from prediction scores