generic_classifier.py 文件源码

def _update_onco_metrics(self, y_true, y_pred, prob):
        self.onco_gene_pred = pd.Series(y_pred, self.y.index)
        self.onco_gene_score = pd.Series(prob, self.y.index)

        # compute metrics for classification
        self.onco_gene_count[self.num_pred] = sum(y_pred)
        prec, recall, fscore, support = metrics.precision_recall_fscore_support(y_true, y_pred)
        self.onco_precision[self.num_pred] = prec[self.onco_num]
        self.onco_recall[self.num_pred] = recall[self.onco_num]
        self.onco_f1_score[self.num_pred] = fscore[self.onco_num]
        self.logger.debug('Onco Iter %d: Precission=%s, Recall=%s, f1_score=%s' % (
                          self.num_pred + 1, str(prec), str(recall), str(fscore)))

        # compute ROC curve metrics
        fpr, tpr, thresholds = metrics.roc_curve(y_true, prob)
        self.onco_tpr_array[self.num_pred, :] = interp(self.onco_fpr_array, fpr, tpr)
        #self.onco_mean_tpr[0] = 0.0

        # compute Precision-Recall curve metrics
        p, r, thresh = metrics.precision_recall_curve(y_true, prob)
        p, r, thresh = p[::-1], r[::-1], thresh[::-1]  # reverse order of results
        thresh = np.insert(thresh, 0, 1.0)
        self.onco_precision_array[self.num_pred, :] = interp(self.onco_recall_array, r, p)
        self.onco_threshold_array[self.num_pred, :] = interp(self.onco_recall_array, r, thresh)