Compute noise_variance_ in PCA implementation
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jcrist
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jcrist
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Jan 17, 2025
| @@ -128,9 +128,6 @@ void fit_impl(raft::handle_t& handle, | |||
| streams, | |||
| n_streams, | |||
| verbose); | |||
| for (std::uint32_t i = 0; i < n_streams; i++) { | |||
| handle.sync_stream(streams[i]); | |||
| } | |||
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This sync point was unneeded, it was already handled after exiting this branch below.
| true, | ||
| stream); | ||
| } else { | ||
| raft::matrix::setValue(noise_vars, noise_vars, math_t{0}, 1, stream); |
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noise_vars is a len-1 device array that I want to set to 0. This seems to work (and is used at least one other place in cuml already), but I'm honestly not sure if it's the best spelling of that. In particular, I don't understand why a method for filling an output array with a single value also takes in an input array (which is the same as the output array in all calling locations I can find).
Previously `noise_vars` was an output parameter passed to the cuda PCA implementation, but it was unimplemented. This adds support for computing `noise_vars` in the cuda code, and tests that the results are valid by comparing to the scikit-learn implementation. The previous code would always have a `noise_variance_` of 0, resulting in downstream issues interpreting results after converting a cuml estimator to its sklearn equivalent (e.g. broken `score_samples`).
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Previously
noise_varswas an output parameter passed to the cuda PCA implementation, but it was unimplemented. This adds support for computingnoise_varsin the cuda code, and tests that the results are valid by comparing to the scikit-learn implementation.The previous code would always have a
noise_variance_of 0, resulting in downstream issues interpreting results after converting a cuml estimator to its sklearn equivalent (e.g. brokenscore_samples).