How to store scaling parameters for later use

you can use sklearn's built-in tool:

from sklearn.externals import joblib

scaler_filename = "scaler.save"

joblib.dump(scaler, scaler_filename)

# And now to load...

scaler = joblib.load(scaler_filename)

注意： from sklearn.preprocessing import MinMaxScaler 中的 MinMaxScaler 只接受shape为 [n, 1] 的数据的缩放， [1, n]的shape的数据是不能缩放的（缩放所得数据会出错）：

https://stackoverflow.com/questions/25886116/sklearns-minmaxscaler-only-returns-zeros

问题：

I am trying to scale a some number to a range of 0 - 1 using preprocessing from sklearn. Thats what i did:

data = [44.645, 44.055, 44.54, 44.04, 43.975, 43.49, 42.04, 42.6, 42.46, 41.405]

min_max_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))

data_scaled = min_max_scaler.fit_transform([data])

print data_scaled

But data_scaled only contains zeros. What am i doing wrong?

回答：

I had the same problem when I tried scaling with MinMaxScaler from sklearn.preprocessing. Scaler returned me zeros when I used a shape a numpy array as list, i.e. [1, n]. Input array would looked in your case like

data = [[44.645, 44.055, 44.54, 44.04, 43.975, 43.49, 42.04, 42.6, 42.46, 41.405]]

I changed the shape of array to [n, 1]. I your case it would be

data = [[44.645],

        [44.055],

        [44.540],

        [44.040],

        [43.975],

        [43.490],

        [42.040],

        [42.600],

        [42.460],

        [41.405]]

Then MinMaxScaler worked in proper way.