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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/06/14 04:58:13 UTC
[GitHub] leezu opened a new issue #11271: nd.topk regression with nan values
leezu opened a new issue #11271: nd.topk regression with nan values
URL: https://github.com/apache/incubator-mxnet/issues/11271
mxnet master contains a regression in the nd.topk operator compared to mxnet v1.2. Likely https://github.com/apache/incubator-mxnet/pull/10997 is at fault (but I have not performed an bisection to proof that).
Consider the following:
Behavior in mxnet master:
```
In [1]: a = [np.nan] * 4 + list(range(2500))
...: a = mx.nd.array(a)
...: print(a)
...: for k in range(3,10):
...: print(mx.nd.topk(a, k=k))
...:
...:
[ nan nan nan ... 2497. 2498. 2499.]
<NDArray 2504 @cpu(0)>
[2. 0. 1.]
<NDArray 3 @cpu(0)>
[2. 0. 1. 3.]
<NDArray 4 @cpu(0)>
[2. 4. 0. 1. 3.]
<NDArray 5 @cpu(0)>
[5. 4. 2. 0. 1. 3.]
<NDArray 6 @cpu(0)>
[2. 6. 5. 4. 0. 1. 3.]
<NDArray 7 @cpu(0)>
[ 2. 1. 10. 9. 8. 6. 0. 3.]
<NDArray 8 @cpu(0)>
[ 2. 1. 11. 10. 9. 6. 8. 0. 3.]
<NDArray 9 @cpu(0)>
```
Behavior in mxnet v1.2:
```
In [1]: In [1]: a = [np.nan] * 4 + list(range(2500))
...: ...: a = mx.nd.array(a)
...: ...: print(a)
...: ...: for k in range(3,10):
...: ...: print(mx.nd.topk(a, k=k))
...:
[ nan nan nan ... 2497. 2498. 2499.]
<NDArray 2504 @cpu(0)>
[0. 1. 2.]
<NDArray 3 @cpu(0)>
[0. 1. 2. 3.]
<NDArray 4 @cpu(0)>
[0.000e+00 1.000e+00 2.000e+00 3.000e+00 2.503e+03]
<NDArray 5 @cpu(0)>
[0.000e+00 1.000e+00 2.000e+00 3.000e+00 2.503e+03 2.502e+03]
<NDArray 6 @cpu(0)>
[0.000e+00 1.000e+00 2.000e+00 3.000e+00 2.503e+03 2.502e+03 2.501e+03]
<NDArray 7 @cpu(0)>
[0.000e+00 1.000e+00 2.000e+00 3.000e+00 2.503e+03 2.502e+03 2.501e+03
2.500e+03]
<NDArray 8 @cpu(0)>
[0.000e+00 1.000e+00 2.000e+00 3.000e+00 2.503e+03 2.502e+03 2.501e+03
2.500e+03 2.499e+03]
<NDArray 9 @cpu(0)>
```
Notice how the result is correct with mxnet version 1.2 but wrong on the master version. As a sidenote, even with mxnet version 1.2 the behavior of nd.topk is inconsistent with nd.max. The latter ignores 'nan' whereas the former considers it to be the maximum element.
```
n [9]: mx.nd.argmax(mx.nd.array([np.nan, 1]), axis=0)
Out[9]:
[1.]
<NDArray 1 @cpu(0)>
In [10]: mx.nd.topk(mx.nd.array([np.nan, 1]), axis=0)
Out[10]:
[0.]
<NDArray 1 @cpu(0)>
```
Arguably there was never a guarantee that nd.topk works under the presence of nan values, but legacy code may rely on this behavior and can break in unexpected ways.
@asmushetzel
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