How to standard scale a 3D matrix?

Question

I am working on a signal classification problem and would like to scale the dataset matrix first, but my data is in a 3D format (batch, length, channels).
I tried to use Scikit-learn Standard Scaler:

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

But I've got this error message:

Found array with dim 3. StandardScaler expected <= 2

I think one solution would be to split the matrix by each channel in multiples 2D matrices, scale them separately and then put back in 3D format, but I wonder if there is a better solution.
Thank you very much.

Answer 1

You'll have to fit and store a scaler for each channel

from sklearn.preprocessing import StandardScaler

scalers = {}
for i in range(X_train.shape[1]):
    scalers[i] = StandardScaler()
    X_train[:, i, :] = scalers[i].fit_transform(X_train[:, i, :]) 

for i in range(X_test.shape[1]):
    X_test[:, i, :] = scalers[i].transform(X_test[:, i, :])