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
I changed the shape of array to [n, 1]. I your case it would be
Then MinMaxScaler worked in proper way. |
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