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

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Answer 1

The best way to do this is to create an ML pipeline like the following:

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.externals import joblib


pipeline = make_pipeline(MinMaxScaler(),YOUR_ML_MODEL() )

model = pipeline.fit(X_train, y_train)

Now you can save it to a file:

joblib.dump(model, 'filename.mod') 

Later you can load it like this:

model = joblib.load('filename.mod')

Answer 2

You can use pickle, to save the scaler:

import pickle
scalerfile = 'scaler.sav'
pickle.dump(scaler, open(scalerfile, 'wb'))

Load it back:

import pickle
scalerfile = 'scaler.sav'
scaler = pickle.load(open(scalerfile, 'rb'))
test_scaled_set = scaler.transform(test_set)

Answer 3

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

Answer 4

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

Answer 5

Just a note that sklearn.externals.joblib has been deprecated and is superseded by plain old joblib, which can be installed with pip install joblib:

import joblib
joblib.dump(my_scaler, 'scaler.gz')
my_scaler = joblib.load('scaler.gz')

Note that file extensions can be anything, but if it is one of ['.z', '.gz', '.bz2', '.xz', '.lzma'] then the corresponding compression protocol will be used. Docs for joblib.dump() and joblib.load() methods.