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@sethah Since issue of `labelCol` has been resoloved, I continue to work on
this.
Like casting of `labelCol`, I make the casting of `weightCol` happens in
`Predictor.fit()`. What's your
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Yeah, the original fix was small and so it was ok to include it here, but
we are expanding the scope of the change so I think it's best to separate it.
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@srowen Feel free. It was a small fix that only modify `getNumClasses` in
that jira before we tend to move cast to `Predictor`.
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@sethah that's my fault, as I originally told @zhengruifeng that I
preferred to make these changes together. They sound closely related, but, if
you all think it should be separate after looking
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@sethah OK, I will open a new JIRA about labelCol.
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Maybe we can solve the label column issue first? Would you mind opening a
new Jira/PR? I'm happy to hear other opinions as well :)
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@sethah ok, I will revert this PR to only focus on: 1, add test for
WeightCol in MLTestingUtils.checkNumericTypes; 2, add cast for WeightCol in
each algo; 3, add cast in `getNumClasses` to
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I strongly prefer to move the issue with the label column into its own
Jira/PR. They are different changes and I think the label column issues are
large enough to warrant their own considerations.
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@sethah @BenFradet I have moved the cast of `LabelCol` and `WeightCol` (if
any) into `Predict.fit`. And now algos only need to deal with casted dataframe
in its `train()` method.
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@sethah Moving the cast to `Predictor.fit` seems like a good idea. It'd
remove the reliance on `extractLabeledPoints` which is not used by every
algorithm (eg LR).
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@sethah @srowen I think we can firstly work on this PR to support weightCol
numeric types and by the way fix the bug in LabelCol.
And then, open another jira to discuss whether cast
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The problem with the classifier tests failing reveals a more fundamental
issue. We tackle the numeric label problem by having each algorithm convert the
label column to `DoubleType`. That means that
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After discussion with @srowen , I merge the fix of 'getNumClasses' into
this PR.
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@sethah DecisionTreeClassifierSuite, NaiveBayesSuite,
RandomForestClassifierSuite fail due to `Classifier.getNumClasses()` without
meta do not support non-double numeric label.
I think
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@sethah I have add weighCol datatype checking into `checkNumericTypes`.
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@zhengruifeng We already test the label col using `checkNumericTypes` in
`MLTestingUtils`. Temporary tests are ok with me but they still need to test
every single numeric type, in each test suite.
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