SVM hack

convert_svm.py

from scipy.sparse import csr_matrix
import numpy as np

def make_sparse(clf):
    """
    Make sklearn.svm.SVC trained on dense data work on sparse features without fitting if again.
    """
    clf._sparse = True
    clf.support_vectors_ = csr_matrix(clf.support_vectors_)

    # From https://github.com/scikit-learn/scikit-learn/blob/14031f65d144e3966113d3daec836e443c6d7a5b/sklearn/svm/base.py#L288-L293
    n_class = len(clf._label) - 1
    n_SV = clf.support_vectors_.shape[0]

    dual_coef_indices = np.tile(np.arange(n_SV), n_class)
    dual_coef_indptr = np.arange(0, dual_coef_indices.size + 1,
                                 dual_coef_indices.size / n_class)
    clf.dual_coef_ = csr_matrix(
        (clf.dual_coef_.ravel(), dual_coef_indices, dual_coef_indptr),
        (n_class, n_SV))
    return clf