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vectorized relabeling of numpy 1-d

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I have a huge training dataset with 4 classes. These classes are labeled non-consecutively. To be able to apply a sequential neural network the classes have to be relabeled so that the unique values in the classes are consecutive. In addition, at the end of the script I have to relabel them back to their old values.

I know how to relabel them with loops:

def relabel(old_classes, new_classes): indexes=[np.where(old_classes ==np.unique(old_classes)[i]) for i in range(len(new_classes))] for i in range(len(new_classes )): old_classes [indexes[i]]=new_classes[i]>>> old_classes = np.array([0,1,2,6,6,2,6,1,1,0])>>> new_classes = np.arange(len(np.unique(old_classes)))>>> relabel(old_classes,new_classes)array([0, 1, 2, 3, 3, 2, 3, 1, 1, 0])

But this isn't nice coding and it takes quite a lot of time.

Any idea how to vectorize this relabeling?

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edited 1 min ago asked 12 mins ago

Wilmar van Ommeren 16910

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Use optional argument return_inverse with np.unique -

np.unique(old_classes,return_inverse=1)[1]

share answered 7 mins ago

Divakar 86k103575 Â Â Â But can i also use this to relabel them back to their old values? âÂ

Wilmar van Ommeren 5 mins ago Â Â Â @WilmarvanOmmeren Did you try it? This would re-label same as what you are acheveing with your relabel func, unless you are talking about some other kind of relabeling? âÂ

Divakar 4 mins ago

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