Vectorized Relabeling Of Numpy Array To Consecutive Numbers And Retrieving Back

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

Solution 1:

We can use the optional argument return_inverse with np.unique to get those unique sequential IDs/tags, like so -

unq_arr, unq_tags = np.unique(old_classes,return_inverse=1)

Index into unq_arr with unq_tags to retrieve back -

old_classes_retrieved = unq_arr[unq_tags] 

Sample run -

In [69]: old_classes = np.array([0,1,2,6,6,2,6,1,1,0])

In [70]: unq_arr, unq_tags = np.unique(old_classes,return_inverse=1)

In [71]: unq_arr
Out[71]: array([0, 1, 2, 6])

In [72]: unq_tags
Out[72]: array([0, 1, 2, 3, 3, 2, 3, 1, 1, 0])

In [73]: old_classes_retrieved = unq_arr[unq_tags]

In [74]: old_classes_retrieved
Out[74]: array([0, 1, 2, 6, 6, 2, 6, 1, 1, 0])