Make PCA projection reproducible given seed#46
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sklearn auto-selects the randomized SVD solver for large inputs, calling PCA(n_components=2) without `random_state` produced a different projection on every run. Now fixed by passing the seed to the API call. cuML PCA is left unchanged: it has no random_state parameter (passing one raises TypeError and silently falls back to sklearn), and its full-SVD solver is already deterministic. Verified on a V100: cuML PCA stays on GPU path and is reproducible run-to-run.
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sklearn auto-selects the randomized SVD solver for large inputs, calling PCA(n_components=2) without
random_stateproduced a different projection on every run. Now fixed by passing the seed to the API call.cuML PCA is left unchanged: it has no
random_stateparameter (passing one raises TypeError and silently falls back to sklearn), and its full-SVD solver is already deterministic. Verified on a V100: cuML PCA stays on GPU path and is reproducible run-to-run. Changing the solver to "jacobi" doesn't make sense for the interactive scale data with 768 dim.