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Add analysis scripts and experiment configurations for bridge attention and sensitivity studies

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- Introduced `bridge_attention_ceiling_check.py` for variance decomposition analysis on bridge attention configurations.
- Added `bridge_attention_readout.py` to perform per-tower gate and contribution readouts, including AUC sanity checks.
- Created multiple JSON configuration files for backbone replication experiments, including anonymous CV variants and basic backbones.
- Implemented sensitivity experiments to evaluate the impact of axial length inclusion and EfficientNetV2-M performance at higher resolutions.
- Added a memory probe script to assess GPU memory usage during training with EfficientNetV2-M.
This commit is contained in:
rpotter6298
2026-07-03 08:51:44 +02:00
parent 3d954a4606
commit 708fbc70ce
52 changed files with 2223 additions and 218 deletions
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v4/classes/v4_hypertower.py
+18 -6
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@@ -359,21 +359,28 @@ def main():
if save_predictions and eval_stage_preds:
# Collect all unique entity_ids across val+test sets of all folds.
# Preserve the natural dtype of y so regression targets keep their
# fractional values (casting to int silently rounds VF_MD).
seen, all_ids, id_to_y = set(), [], {}
y_is_float = False
for fp in eval_stage_preds:
for eid, y in zip(fp["val_ids"], fp["val_y"]):
k = str(eid)
if k not in seen:
seen.add(k); all_ids.append(eid)
id_to_y[k] = int(y)
y_is_float = y_is_float or np.issubdtype(np.asarray(y).dtype, np.floating)
id_to_y[k] = float(y) if y_is_float else int(y)
if fp.get("test_ids"):
for eid, y in zip(fp["test_ids"], fp["test_y"]):
k = str(eid)
if k not in seen:
seen.add(k); all_ids.append(eid)
id_to_y[k] = int(y)
y_is_float = y_is_float or np.issubdtype(np.asarray(y).dtype, np.floating)
id_to_y[k] = float(y) if y_is_float else int(y)
y_true = np.array([id_to_y.get(str(e), -1) for e in all_ids], dtype=np.int64)
sentinel = float("nan") if y_is_float else -1
dtype = np.float64 if y_is_float else np.int64
y_true = np.array([id_to_y.get(str(e), sentinel) for e in all_ids], dtype=dtype)
store = PredictionStore(n_folds=len(eval_stage_preds), n_classes=num_classes)
store.register_phase(
phase=eval_stage,
@@ -402,19 +409,24 @@ def main():
for phase, phase_preds in all_phase_preds.items():
emb_dim = phase_preds[0]["val_z"].shape[-1]
seen, all_ids, id_to_y = set(), [], {}
y_is_float = False
for fp in phase_preds:
for eid, y in zip(fp["val_ids"], fp["val_y"]):
k = str(eid)
if k not in seen:
seen.add(k); all_ids.append(eid)
id_to_y[k] = int(y)
y_is_float = y_is_float or np.issubdtype(np.asarray(y).dtype, np.floating)
id_to_y[k] = float(y) if y_is_float else int(y)
if fp.get("test_ids"):
for eid, y in zip(fp["test_ids"], fp["test_y"]):
k = str(eid)
if k not in seen:
seen.add(k); all_ids.append(eid)
id_to_y[k] = int(y)
y_true = np.array([id_to_y.get(str(e), -1) for e in all_ids], dtype=np.int64)
y_is_float = y_is_float or np.issubdtype(np.asarray(y).dtype, np.floating)
id_to_y[k] = float(y) if y_is_float else int(y)
sentinel = float("nan") if y_is_float else -1
dtype = np.float64 if y_is_float else np.int64
y_true = np.array([id_to_y.get(str(e), sentinel) for e in all_ids], dtype=dtype)
fstore.register_phase(phase=phase, entity_ids=all_ids, y_true=y_true)
fstore.register_head(phase=phase, head=f"{phase}_embedding",
n_epochs=1, embedding_dim=emb_dim)