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Make PCA projection reproducible given seed #46

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Make PCA projection reproducible given seed #46

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6 changes: 5 additions & 1 deletion shared/utils/clustering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -199,7 +199,9 @@ def _reduce_dim_sklearn(embeddings: np.ndarray, method: str, seed: Optional[int]
effective_workers = -1 if n_workers > 1 else n_workers

if method.upper() == "PCA":
reducer = PCA(n_components=2)
# Pass random_state so the randomized SVD solver (auto-selected for
# large inputs) is reproducible when a seed is set; None keeps it random.
reducer = PCA(n_components=2, random_state=seed)
elif method.upper() == "TSNE":
# Adjust perplexity to be valid for the sample size
n_samples = embeddings.shape[0]
Expand Down Expand Up @@ -244,6 +246,8 @@ def _reduce_dim_cuml(embeddings: np.ndarray, method: str, seed: Optional[int], n

if method.upper() == "PCA":
from cuml.decomposition import PCA as cuPCA
# cuML PCA takes no random_state and needs none: its full-SVD solver
# is deterministic, so results are already reproducible run-to-run.
reducer = cuPCA(n_components=2)
elif method.upper() == "TSNE":
from cuml.manifold import TSNE as cuTSNE
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