Save MinMaxScaler model in sklearn

Question

I'm using the MinMaxScaler model in sklearn to normalize the features of a model.

training_set = np.random.rand(4,4)*10
training_set

       [[ 6.01144787,  0.59753007,  2.0014852 ,  3.45433657],
       [ 6.03041646,  5.15589559,  6.64992437,  2.63440202],
       [ 2.27733136,  9.29927394,  0.03718093,  7.7679183 ],
       [ 9.86934288,  7.59003904,  6.02363739,  2.78294206]]


scaler = MinMaxScaler()
scaler.fit(training_set)    
scaler.transform(training_set)


   [[ 0.49184811,  0.        ,  0.29704831,  0.15972182],
   [ 0.4943466 ,  0.52384506,  1.        ,  0.        ],
   [ 0.        ,  1.        ,  0.        ,  1.        ],
   [ 1.        ,  0.80357559,  0.9052909 ,  0.02893534]]

Now I want to use the same scaler to normalize the test set:

   [[ 8.31263467,  7.99782295,  0.02031658,  9.43249727],
   [ 1.03761228,  9.53173021,  5.99539478,  4.81456067],
   [ 0.19715961,  5.97702519,  0.53347403,  5.58747666],
   [ 9.67505429,  2.76225253,  7.39944931,  8.46746594]]

But I don't want so use the scaler.fit() with the training data all the time. Is there a way to save the scaler and load it later from a different file?

Answer 1

Even better than pickle (which creates much larger files than this method), 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) 

Note: sklearn.externals.joblib is deprecated. Install and use the pure joblib instead