Github user srowen commented on the issue:
https://github.com/apache/spark/pull/15214
Merged to master
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Github user mpjlu commented on the issue:
https://github.com/apache/spark/pull/15214
Hi @srowen , sorry for forgetting update the doc and python/ml/feature.py
in last PR.
This pr has added ml/feature.py. It looks good to me.
Thanks
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Github user mpjlu commented on the issue:
https://github.com/apache/spark/pull/15214
Thanks, this looks good to me.
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Github user yanboliang commented on the issue:
https://github.com/apache/spark/pull/15214
And you can also refer all other Estimator in ML, even you swap the
arguments setting order, you still get the same model. Thanks.
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Github user yanboliang commented on the issue:
https://github.com/apache/spark/pull/15214
@srowen @mpjlu
Another important reason for this change: it's error prone for Python ML
API.
```
def __init__(self, numTopFeatures=50, featuresCol="features",
outputCol=None,
Github user srowen commented on the issue:
https://github.com/apache/spark/pull/15214
OK, I could also support either behavior. After all, for any component,
`.setFoo(x).setFoo(y)` also creates a different model if the order is swapped,
so I am not so clear that's a 'problem'.
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Github user mpjlu commented on the issue:
https://github.com/apache/spark/pull/15214
hi @srowen .
My understand of yanbo's comments here is,
if user use chSqSelector like this:
model1 = new ChiSqSelector().setFPR(0.05).setKBest(100).fit(data)
model2 = new
Github user srowen commented on the issue:
https://github.com/apache/spark/pull/15214
I'm OK with it. @mpjlu sounds like you approve?
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Github user mpjlu commented on the issue:
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Hi @yanboliang , got it. Thanks.
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Github user AmplabJenkins commented on the issue:
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Github user yanboliang commented on the issue:
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@mpjlu The most important cause of this change is that the fit/train model
should not dependent on the order of users setting params. In other words,
users should get the same model whether set
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Github user mpjlu commented on the issue:
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Hi @srowen and @yanboliang ; Thanks for your following up PR.
I partly agree with your comments on 17017.
**1. "if users both set numTopFeatures and percentile, it will train kbest
or
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Github user srowen commented on the issue:
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Oh I see. I trust your judgment on this, just wish we could have gotten
your review on the original PR. @mpjlu what do you think?
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