tlby commented on issue #14461: [MXNET-1359] Adds a multiclass-MCC metric derived from Pearson URL: https://github.com/apache/incubator-mxnet/pull/14461#issuecomment-475067430 ```diff diff --git a/python/mxnet/metric.py b/python/mxnet/metric.py index 2a33cf4d9..6de76cc64 100644 --- a/python/mxnet/metric.py +++ b/python/mxnet/metric.py @@ -1576,9 +1576,8 @@ class PCC(EvalMetric): n = max(pred.max(), label.max()) if n >= self.k: self._grow(n + 1 - self.k) - bcm = numpy.zeros((self.k, self.k)) - for i, j in zip(pred, label): - bcm[i, j] += 1 + ident = numpy.identity(self.k) + bcm = numpy.tensordot(ident[label], ident[pred].T, axes=(0,1)) self.lcm += bcm self.gcm += bcm ``` seems more efficient for constructing the confusion matrix, but benchmarks worse. I'm new to NumPy though, anyone see a better approach?
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