scikit learn SVM, how to save/load support vectors?

Question

using python scikit svm, after running clf.fit(X, Y), you get your support vectors. could I load these support vectors directly (passing them as paramter) when instantiate a svm.SVC object? which means I do not need to running fit() method each time to do predication

Answer 1

From the scikit manual: http://scikit-learn.org/stable/modules/model_persistence.html

1.2.4 Model persistence It is possible to save a model in the scikit by using Python’s built-in persistence model, namely pickle.

>>> from sklearn import svm
>>> from sklearn import datasets
>>> clf = svm.SVC()
>>> iris = datasets.load_iris()
>>> X, y = iris.data, iris.target
>>> clf.fit(X, y)
SVC(kernel=’rbf’, C=1.0, probability=False, degree=3, coef0=0.0, eps=0.001,
cache_size=100.0, shrinking=True, gamma=0.00666666666667)
>>> import pickle
>>> s = pickle.dumps(clf)
>>> clf2 = pickle.loads(s)
>>> clf2.predict(X[0])
array([ 0.])
>>> y[0]
0

In the specific case of the scikit, it may be more interesting to use joblib’s replacement of pickle, which is more efficient on big data, but can only pickle to the disk and not to a string:

>>> from sklearn.externals import joblib
>>> joblib.dump(clf, ’filename.pkl’)