String kernels and scikit-learn SVM

I'm looking to use scikit-learn's SVM libraries with a custom string kernel. I have a string kernel function that computes the distance between strings, like so:

>>> substring_kernel("this is a test string", "so is this")
0.6765380761985251

>>> substring_kernel("a different string", "and another")
0.2638510175945378

(Essentially, it's just a function that takes two strings as input and returns a float.) And I have some test data:

X = array(["test sentence for SVM", "another example"])
Y = array([0, 1])

I want to train a SVM classifier on this data, like so:

from sklearn import svm
svm_model = svm.SVC(kernel=substring_kernel)
svm_model.fit(X, Y)

However, this returns the error:

>>> svm_model.fit(X, Y)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/svm/base.py", line 139, in fit
    X = atleast2d_or_csr(X, dtype=np.float64, order='C')
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.py", line 134, in atleast2d_or_csr
    "tocsr", force_all_finite)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.py", line 111, in _atleast2d_or_sparse
    force_all_finite=force_all_finite)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.py", line 91, in array2d
    X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/numeric.py", line 320, in asarray
    return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: test sentence for SVM

As far as I can tell, scikit-learn's implementation of SVM is the root cause of the error because it tries to coerce all attributes to numpy.float64 (but I'm not completely sure that this is the case). So, what exactly is going on, and can the problem be avoided?