MinmaxScaler：规范化 4D 输入数组

Question

I have a 4D array of input that I would like to normalise using MinMaxScaler . For simplicity, I give an example with the following array:

A = np.array([
            [[[0, 1, 2, 3],
              [3, 0, 1, 2],
              [2, 3, 0, 1],
              [1, 3, 2, 1],
              [1, 2, 3, 0]]],
            
            [[[9, 8, 7, 6],
              [5, 4, 3, 2],
              [0, 9, 8, 3],
              [1, 9, 2, 3],
              [1, 0, -1, 2]]],
            
            [[[0, 7, 1, 2],
              [1, 2, 1, 0],
              [0, 2, 0, 7],
              [-1, 3, 0, 1],
              [1, 0, 1, 0]]]
              ])
A.shape
(3,1,5,4)

In the given example, the array contains 3 input samples, where each sample has the shape (1,5,4) . Each column of the input represents 1 variable (feature), so each sample has 4 features .

I would like to normalise the input data, But MinMaxScaler expects a 2D array (n_samples, n_features) like dataframe.

How then do I use it to normalise this input data?

Answer 1

Vectorize the data

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()

A_sq = np.squeeze(A)

print(A_sq.shape)
# (3, 5, 4)

scaler.fit(np.squeeze(A_sq).reshape(3,-1)) # reshape to (3, 20)
#MinMaxScaler()

Answer 2

You can use the below code to normalize 4D array.

from sklearn.preprocessing import MinMaxScaler, StandardScaler
scaler = MinMaxScaler(feature_range=(0, 1))
def norm(arr):
    arrays_list=list()
    objects_list=list()
    for i in range(arr.shape[0]):
        temp_arr=arr[i]
        temp_arr=temp_arr[0]
        scaler.fit(temp_arr)
        temp_arr=scaler.transform(temp_arr)
        objects_list.append(scaler)
        arrays_list.append([temp_arr])
    return objects_list,np.array(arrays_list)
      

pass the array to the function like

objects,array=norm(A)

it will return a list of MinMax objects and your original array with normalize values.

Output:

Answer 3

" If you want a scaler for each channel, you can reshape each channel of the data to be of shape (10000, 5*5). Each channel (which was previously 5x5) is now a length 25 vector, and the scaler will work. You'll have to transform your evaluation data in the same way with the scalers in channel_scalers." Maybe this will help, not sure if this is what you're looking for exactly, but... Python scaling with 4D data