[Feature] Basic utils to handle raw data features #2102
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VoVAllen
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Aug 25, 2020
| else: | ||
| return feat | ||
|
|
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| def parse_category_multi_feat(category_inputs, norm=None): |
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Would user need the corresponding labels of the multi-hot vector (i.e. The first elements is for label 'A')?
| manager = Manager() | ||
| d = manager.dict() | ||
| job=[] | ||
| for i in range(num_process): |
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You can use multiprocessing pool(pool.map) here.
|
And for converting feature into one-hot/multi-hot, if the category information is needed, I think it might be better to design it as a class, just like what sklearn do. |
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This reverts commit 33a8bb9.
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Description
We provide a list of APIs in data.utils to handle raw data into numpy arrays:
#2088
Checklist
Please feel free to remove inapplicable items for your PR.
or have been fixed to be compatible with this change
Changes