Switch default optimizer to CG with multi-start#1284
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Marius1311 wants to merge 2 commits intomainfrom
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Switch default optimizer to CG with multi-start#1284Marius1311 wants to merge 2 commits intomainfrom
Marius1311 wants to merge 2 commits intomainfrom
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Allow users to choose the optimization method for the GPCCA rotation matrix via the new 'optimizer' parameter. Valid options: 'Nelder-Mead' (default), 'L-BFGS-B', 'BFGS', 'CG'. Warns when using Nelder-Mead with n_states > 15 as it scales poorly.
- Change default optimizer from Nelder-Mead to CG (conjugate gradient) - Change default n_starts from 1 to 10 with perturbation_scale=0.1 - Add n_starts, perturbation_scale, seed parameters to compute_macrostates - Forward optimizer parameters from fit() to compute_macrostates() - Update docstrings with factual complexity descriptions - Add .gitignore entries for uv.lock and .github/prompts/
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Summary
Switch the default GPCCA rotation optimizer from Nelder-Mead to conjugate gradient (CG) with 10 random restarts. This makes
compute_macrostates()scale to large numbers of macrostates (m > 15) where Nelder-Mead becomes impractical.Changes
"Nelder-Mead"→"CG"1→10(withperturbation_scale=0.1)compute_macrostates():n_starts,perturbation_scale,seed— these were already supported by pyGPCCA but not exposed in CellRankfit()method: Now forwards optimizer parameters tocompute_macrostates()(previously always used hardcoded defaults)uv.lockand.github/prompts/Benchmark results
Validated on pancreas (2,531 cells) and bone marrow (5,780 cells) datasets:
Dependencies
Requires pyGPCCA from
msmdev/pyGPCCAbranchfeature/gradient-optimizationwhich implements the analytical Jacobian and multi-start SO(k) rotation perturbation.