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Check out the excellent docs of sklearn.

如您所见，支持 partial_fit()！这允许在线缩放/小批量缩放，您可以控制小批量！

As you see, there is support for partial_fit()! This allows online-scaling/minibatch-scaling and you can control the minibatches!

示例:

import numpy as np
from sklearn.preprocessing import MinMaxScaler

a = np.array([[1,2,3]])
b = np.array([[10,20,30]])
c = np.array([[5, 10, 15]])

""" Scale on all datasets together in one batch """
offline_scaler = MinMaxScaler()
offline_scaler.fit(np.vstack((a, b, c)))                # fit on whole data at once
a_offline_scaled = offline_scaler.transform(a)
b_offline_scaled = offline_scaler.transform(b)
c_offline_scaled = offline_scaler.transform(c)
print('Offline scaled')
print(a_offline_scaled)
print(b_offline_scaled)
print(c_offline_scaled)

""" Scale on all datasets together in minibatches """
online_scaler = MinMaxScaler()
online_scaler.partial_fit(a)                            # partial fit 1
online_scaler.partial_fit(b)                            # partial fit 2
online_scaler.partial_fit(c)                            # partial fit 3
a_online_scaled = online_scaler.transform(a)
b_online_scaled = online_scaler.transform(b)
c_online_scaled = online_scaler.transform(c)
print('Online scaled')
print(a_online_scaled)
print(b_online_scaled)
print(c_online_scaled)

输出:

Offline scaled
[[ 0.  0.  0.]]
[[ 1.  1.  1.]]
[[ 0.44444444  0.44444444  0.44444444]]
Online scaled
[[ 0.  0.  0.]]
[[ 1.  1.  1.]]
[[ 0.44444444  0.44444444  0.44444444]]