posterior.py 文件源码

def classifier_accuracy_report(self, prediction_vector, threshold=0.5):
        """ Determine AUC and other metrics, write report.

        prediction_vector: vector of booleans (or outcome
        probabilities) of length n_subjects,
        e.g. self.point_predictions, self.ensemble_probabilities()...
        If this has dtype other than bool, prediction_vector > threshold
        is used for the confusion matrix.

        Returns: one string (multiple lines joined with \n, including
        trailing newline) containing a formatted report.

        """
        auc = roc_auc_score(self.model.data.y.astype(float), prediction_vector.astype(float))
        if not (prediction_vector.dtype == np.bool):
            prediction_vector = prediction_vector >= threshold
        conf = confusion_matrix(self.model.data.y, prediction_vector)

        lines = ['AUC: %.3f' % auc,
                 'Confusion matrix: \n\t%s' % str(conf).replace('\n','\n\t')]
        return '\n'.join(lines) + '\n'


    ######################################## 
    # BAYES-FACTOR-BASED METHODS