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Github user srowen commented on the issue:
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@nicklavers I agree it's not great to assume that the current output is
correct, though I strongly suspect it is. We'd ideally do some analysis to
understand what the expected range of outcomes are
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Github user nicklavers commented on the issue:
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Okay I'll push a new commit
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Github user wangmiao1981 commented on the issue:
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@nicklavers it is a key test. The behavior change should be because you
changed the random function, which causes the output changing. I agree with
@srowen you just have to adjust your expected
Github user nicklavers commented on the issue:
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So if `model.gaussiansDF.show()` is testing a key behavior of the model,
then I don't know what it is, and I don't feel comfortable just changing the
test to make it pass, since that seems to def
Github user srowen commented on the issue:
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I'd expect the Gaussians to move as a result of this change. I think it's
fair to simply modify the test to match the current output in this case.
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Github user wangmiao1981 commented on the issue:
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`model.gaussiansDF.show()` displays the `mean` and `variance` of the
gaussians, which are Dataframes.
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Github user nicklavers commented on the issue:
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and `KMeans` is passing for me
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Github user nicklavers commented on the issue:
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Okay so I've tried a handful of seeds, and none have produced the results
that the test is expecting. The doctests that are failing are
`model.gaussiansDF.show()`, which as far as I can tell is j
Github user srowen commented on the issue:
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If we can get that last test failure patched in any reasonable form, I'll
merge
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Jenkins add to whitelist
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Github user yanboliang commented on the issue:
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@nicklavers Please also change seed for ```GaussianMixture``` doctest in
```python/pyspark/ml/clustering.py```. And check whether we need to change seed
for ```KMeans``` doctest. Thanks.
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thanks
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Github user nicklavers commented on the issue:
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hey @srowen can you start up a new test?
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These seeds passed the tests on my machine, at least
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Okay I'll find some new seeds and commit again
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Github user yanboliang commented on the issue:
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+1 @srowen
I think we can change to another random seed to make it pass.
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Github user srowen commented on the issue:
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Anyway, back to the actual content -- the actual test assertions in the
failing test are correct, in that it's obvious that the first two points and
second two points should cluster together. I expec
Github user nicklavers commented on the issue:
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The doctests in `python/pyspark/mllib/clustering.py` will have to be
changed, too
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Github user nicklavers commented on the issue:
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I mean, I can certainly reverse-engineer a test that passes, but it
wouldn't be very valuable, since I don't know enough about how
`GaussianMixture` is supposed to work to write a good test for i
Github user srowen commented on the issue:
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You're probably right, and I think it's OK to change the test to match the
current output as a result.
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Github user nicklavers commented on the issue:
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Hey @yanboliang, I'm failing `test_gmm` in `python/pyspark/mllib/tests.py`,
and I'm fairly certain it's because the test relies on the bug I'm fixing.
I saw you worked on `GaussianMixture
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Okay I'm looking into this pyspark test failure
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Jenkins test this please
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Anyway the test and change look sound, let's test it.
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Github user nicklavers commented on the issue:
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Thanks @HyukjinKwon!
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