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…s unified. Removed duplicated imports.
…es using smart triu and neighbourhood graph checks
…a range of datapoint numbers
… of row and col via nonzero and dist measure. Plus better csc usage
…eads, for postprocessing
…asets without crashes
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Summary of the changes in this pull request:
Performance changes: vectorized core computations, replaced loop-based batched_pdist with NumPy triu_indices vectorization, vectorized B matrix construction ++
Parallelization : unified parallelism on joblib, replaced raw multiprocess / shared_memory usage in lpca, kpca, best, and post-processing with joblib.Parallel, eliminating shared-memory boilerplate. Also parallelized post-processing and refactored _postprocess into a standalone _postprocess_worker with batched evaluation
Dynamic memory allocation: added _get_available_memory() to auto-detect available RAM (via psutil or PYRATS_MEMORY_LIMIT env var) and batch work accordingly, preventing OOM crashes on large datasets
sklearn-style API rename: renamed parameters to follow sklearn conventions (d→n_components, k→n_neighbors, eta_min→min_cluster_size, max_iter→n_iter, kpca_kernel→kernel, etc.) with conversions for backwards compatibility
Progress bars: added tqdm progress bars (via verbose flag) to PCA, KPCA, intermediate views, and refinement loops; removed raw print statements
CI benchmark workflow: added .github/workflows/benchmark.yml and tests/scripts/benchmark.py for speed testing across platforms
Cleanup: removed duplicate imports, unused procrustes code, and multiprocess dependency; updated pyproject.toml to add tqdm; refreshed example notebooks and README