rpotter6298
3d7777f010
- Introduced `fused_importance_with_axial.py` to evaluate the importance of Axial_Length in the fused-head model. - Created JSON configurations for various experiments excluding zero-importance clinical features: - `cd_solo_bilateral_dropzero.json`: Bilateral clinical-only evaluation. - `cd_solo_single_dropzero.json`: Single-eye clinical-only evaluation. - `ensemble_refugelike_ckpt_dropzero.json`: Ensemble model with dropped zero-importance features. - `ensemble_single_refugelike_dropzero.json`: Single-eye ensemble model with dropped features.
76 lines
2.9 KiB
Python
76 lines
2.9 KiB
Python
"""Re-run the fused-head permutation importance on the with-Axial_Length model.
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Points F8's make_fused_clinical_importance at the sensitivity checkpoints
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(refugelike_ensemble_with_axial_length, 10 reps with save_checkpoints=true and
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exclude_cols=[]) instead of the standard ensemble_refugelike_ckpt run. This
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tests whether Axial_Length lands at ~0 fused-head importance like the other
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four dropzero features, closing the circle on the historical decision to
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exclude it upstream.
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Output is routed to output/S8e_fused_clinical_importance_with_axial.{png,csv}
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so it does not overwrite the main S8e figure.
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Usage:
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python -m v4.scripts.analysis.fused_importance_with_axial [--n-permutations N]
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"""
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from __future__ import annotations
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import argparse
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from pathlib import Path
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from v4.figures import F8_explainability as F8
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from v4.figures.util.loaders import REPO_ROOT
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AXIAL_RUN = (
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REPO_ROOT / "v4" / "results" / "experiments" / "sensitivity"
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/ "refugelike_ensemble_with_axial_length" / "rep00" / "binary"
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)
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--n-permutations", type=int, default=30,
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help="Permutation repeats per feature (default 30).")
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args = ap.parse_args()
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if not AXIAL_RUN.exists():
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raise SystemExit(f"Sensitivity run not found: {AXIAL_RUN}")
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# Point the F8 module at the sensitivity run. rep_base is derived as
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# V4_CKPT_RUN.parent.parent inside make_fused_clinical_importance, so
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# all 10 reps under refugelike_ensemble_with_axial_length are picked up.
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F8.V4_CKPT_RUN = AXIAL_RUN
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# Route the output filenames so we do not overwrite the primary S8e run.
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orig_savefig = F8.plt.Figure.savefig # noqa: E501 (untouched, just noted)
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orig_out_dir = F8.OUT_DIR
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tag = "with_axial"
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# Monkey-patch pandas.DataFrame.to_csv and Figure.savefig only for calls
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# that name the S8e file. Simplest: swap out OUT_DIR and rename the two
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# target filenames post-hoc via a wrapper.
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src_png = orig_out_dir / "S8e_fused_clinical_importance.png"
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src_csv = orig_out_dir / "S8e_fused_clinical_importance.csv"
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dst_png = orig_out_dir / f"S8e_fused_clinical_importance_{tag}.png"
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dst_csv = orig_out_dir / f"S8e_fused_clinical_importance_{tag}.csv"
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# Move originals aside if present, restore after; simpler than hooking I/O.
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def _stash(p: Path):
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return p.rename(p.with_suffix(p.suffix + ".bak")) if p.exists() else None
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stashed = [_stash(src_png), _stash(src_csv)]
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try:
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F8.make_fused_clinical_importance(n_permutations=args.n_permutations)
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if src_png.exists(): src_png.rename(dst_png)
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if src_csv.exists(): src_csv.rename(dst_csv)
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print(f"\nRenamed outputs -> {dst_png.name}, {dst_csv.name}")
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finally:
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for p in stashed:
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if p is not None:
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p.rename(p.with_suffix(""))
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if __name__ == "__main__":
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main()
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