One way to do this is to move to vendorized dependencies into an submodule of numpy itself (e.g., sklearn.externals.joblib, though maybe even a little more indirection than that would be valuable to make it clear that it isn't part of NumPy public API). This would avoid further enlarging the set of namespaces we use.

In any case, I'm perfectly OK with using something like npy_tempita internally, too, as long as we can be sure that we're using NumPy's vendorized version, not whatever version is installed locally. We're not planning to actually install "npy_tempita" when installing numpy (even for dev installs), right?