d58f6cb01803f255f8d42c740bc399d083db4640,python/mxnet/metric.py,PearsonCorrelation,update,#PearsonCorrelation#Any#Any#,1449

Before Change

            check_label_shapes(label, pred, False, True)
            label = label.asnumpy()
            pred = pred.asnumpy()
            pearson_corr = numpy.corrcoef(pred.ravel(), label.ravel())[0, 1]
            self.sum_metric += pearson_corr
            self.global_sum_metric += pearson_corr
            self.num_inst += 1

After Change

        for label, pred in zip(labels, preds):
            check_label_shapes(label, pred, False, True)
            label = label.asnumpy().ravel().astype(numpy.float64)
            pred = pred.asnumpy().ravel().astype(numpy.float64)
            if self.average == "macro":
                pearson_corr = numpy.corrcoef(pred, label)[0, 1]
                self.sum_metric += pearson_corr
                self.global_sum_metric += pearson_corr
                self.num_inst += 1
                self.global_num_inst += 1
            else:
                self.global_num_inst += 1
                self.num_inst += 1
                self._label_nums, self._mean_l, self._sse_l = \
                    self.update_variance(label, self._label_nums, self._mean_l, self._sse_l)
                self.update_cov(label, pred)
                self._pred_nums, self._mean_p, self._sse_p = \
                    self.update_variance(pred, self._pred_nums, self._mean_p, self._sse_p)

    def get(self):
        if self.num_inst == 0:
            return (self.name, float("nan"))

Instances