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?
So I'm actually not an expert with this but from a bit of research and a few helpful links, I think pickle and sklearn.externals.joblib are going to be your friends here.
The package pickle lets you save models or "dump" models to a file.
I think this link is also helpful. It talks about creating a persistence model. Something that you're going to want to try is:
# could use: import pickle... however let's do something else
from sklearn.externals import joblib
# this is more efficient than pickle for things like large numpy arrays
# ... which sklearn models often have.
# then just 'dump' your file
joblib.dump(clf, 'my_dope_model.pkl')
Here is where you can learn more about the sklearn externals.
Let me know if that doesn't help or I'm not understanding something about your model.
Note: sklearn.externals.joblib is deprecated. Install and use the pure joblib instead