2013/7/11 Gad Abraham <[email protected]>:
> I'm very much a sklearn beginner, and I'd like to use FeatureHasher to
> reduce the dimensionality of a numeric matrix. Any hints on how to do this?
> I've seen the examples showing how to use it with text.
You mean the input is a NumPy array? There's no special support for
that, but the following should work (though it may be slow). Let X be
your array and d the desired dimensionality, then:
hasher = FeatureHasher(n_features=d, input_type="pair")
features = map(str, range(X.shape[1]))
Xh = hasher.transform(zip(features, row) for row in X).toarray()
hashes X into Xh of shape (X.shape[0], d).
You might want to look at the random projection module [1], which can
do somewhat similar transforms much more quickly.
[1]
http://scikit-learn.org/stable/modules/random_projection.html#random-projection
--
Lars Buitinck
Scientific programmer, ILPS
University of Amsterdam
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