Github user zhengruifeng commented on the issue:
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This pr is out of date. So I will close it.
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@WeichenXu123 Yes, my concern is that it is confusing if the transform
failure is caused by column conflict by a âinvisibleâ column.
@srowen Agree that it is not perfect if we
Github user WeichenXu123 commented on the issue:
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@srowen yeah, all models exist this issue. Although, a little difference,
for other models, it is very straightforward for user to call
`setRawPrediction` to avoid overwrite the same name
Github user srowen commented on the issue:
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Sure but isn't this the case with any column that any model adds that
already exists -- why is this different?
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Github user WeichenXu123 commented on the issue:
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Oh, do you mean if input df including a column named "rawPrediction", then
it will be overwritten when it transformed by OVSModel ? Looks like reasonable.
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Github user WeichenXu123 commented on the issue:
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Sorry, I haven't understood where is the issue in current master code. The
models here should be `ClassificationModel` and will always have
`rawPrediction` param and have default value
Github user srowen commented on the issue:
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OK, the code in question uses the `rawPredictionCol` from the `models` it
is given. Yes they'd have to be unique for this to make sense, because it adds
those raw prediction columns to the output.
Github user zhengruifeng commented on the issue:
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@srowen Different from the base model (like LoR), OVR and OVRModel do not
have param `rawPredictionCol`.
So if the input dataframe contains a column which has the same name as base
Github user srowen commented on the issue:
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I might be missing something, but the user sets the raw prediction col
name. Now it's going to use a different name than what the user set.
Isn't it simply an error to apply a second model,
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retest this please
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retest this please
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