Fix num_filters_passed to count individually-passing filters - #252
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fnachon wants to merge 9 commits into
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Fix num_filters_passed to count individually-passing filters#252fnachon wants to merge 9 commits into
fnachon wants to merge 9 commits into
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Changes made to run without errors on the Mac MPS device: torch.autocast, number of devices and workers to use on M1-5 chips, workaround for CUDA-specific code, handling of float64 incompatibilities for MPS.
Replace hardcoded torch.autocast("cuda") with device-agnostic
device_type=tensor.device.type in confidence_utils, inverse_fold,
and writer modules introduced in the upstream merge.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Python pickle does not preserve RDKit atom-level SetProp values. When PyTorch DataLoader spawns worker processes (default num_workers=1 on macOS), self.canonicals is pickled and all atom 'name' properties are lost, causing KeyError in process_atom_features. Fix: load all required molecules directly from the moldir zip inside each get_sample() / get_feat() call instead of using the pickled self.canonicals. The moldir zip handle is cached per-process by _get_zipfile(), so there is no repeated I/O overhead. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…ders - Disable pin_memory on MPS (unsupported, causes UserWarning) - Enable persistent_workers when num_workers > 0 (avoids repeated worker init overhead and the PL suggestion warning) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The counter incremented by the cumulative AND of all filter columns seen so far in the loop, not by whether the current filter passed. Once any filter failed, every later increment added 0 regardless of whether the design actually passed those later filters, so the column ends up reporting "count of leading filters passed before the first failure" instead of the intended total pass count. Fixes HannesStark#126 Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Fixes #126.
num_filters_passedincremented by the cumulative AND of all filter columns seen so far in the loop (self.df[filter_cols].all(axis=1)), not by whether the current filter passed. Once any filter failed for a design, every subsequent increment in the loop added 0 regardless of whether the design actually passed those later filters. So the column ends up reporting "count of leading filters passed before the first failure" rather than the intended total pass count. Fixed by incrementing with the current filter's own pass/fail column instead.