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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/logging/prediction_store.py
+26 -14
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@@ -6,19 +6,19 @@ FeatureStore — records embeddings (opt-in); same structure but per-head
HDF5 layout — PredictionStore
------------------------------
/{phase}/logits float32 (n_folds, n_epochs, n_samples, n_heads, n_classes)
/{phase}/head_names str (n_heads,)
/{phase}/y_true int64 (n_samples,)
/{phase}/entity_id_{k} int64|str (n_samples,) — one dataset per id component
/{phase}/split str (n_folds, n_samples)
/{phase}/loss float32 (n_folds, n_epochs)
/{phase}/logits float32 (n_folds, n_epochs, n_samples, n_heads, n_classes)
/{phase}/head_names str (n_heads,)
/{phase}/y_true int64 | float64 (n_samples,) — float64 for regression targets, int64 otherwise
/{phase}/entity_id_{k} int64|str (n_samples,) — one dataset per id component
/{phase}/split str (n_folds, n_samples)
/{phase}/loss float32 (n_folds, n_epochs)
HDF5 layout — FeatureStore
---------------------------
/{phase}/{head_name} float32 (n_folds, n_epochs, n_samples, embedding_dim)
/{phase}/y_true int64 (n_samples,)
/{phase}/entity_id_{k} int64|str (n_samples,)
/{phase}/split str (n_folds, n_samples)
/{phase}/{head_name} float32 (n_folds, n_epochs, n_samples, embedding_dim)
/{phase}/y_true int64 | float64 (n_samples,) — float64 for regression targets, int64 otherwise
/{phase}/entity_id_{k} int64|str (n_samples,)
/{phase}/split str (n_folds, n_samples)
"""
from __future__ import annotations
@@ -35,6 +35,18 @@ except ImportError as e:
_STR_DT = h5py.string_dtype()
def _coerce_y_true(y_true) -> np.ndarray:
"""Coerce y_true to int64 for integer-typed input, float64 otherwise.
Forcing int64 unconditionally would silently round regression targets
(e.g. VF_MD), so we honour float input by storing as float64.
"""
arr = np.asarray(y_true)
if np.issubdtype(arr.dtype, np.floating):
return arr.astype(np.float64)
return arr.astype(np.int64)
# ---------------------------------------------------------------------------
# Internal phase buffer
# ---------------------------------------------------------------------------
@@ -52,7 +64,7 @@ class _PhaseBuffer:
n_s = len(entity_ids)
n_h = len(head_names)
self.entity_ids = list(entity_ids)
self.y_true = np.asarray(y_true, dtype=np.int64)
self.y_true = _coerce_y_true(y_true)
self.head_names = list(head_names)
self.n_epochs = n_epochs
self.logits = np.full((n_folds, n_epochs, n_s, n_h, n_classes), np.nan, dtype=np.float32)
@@ -74,7 +86,7 @@ class _FeaturePhaseBuffer:
n_folds: int,
):
self.entity_ids = list(entity_ids)
self.y_true = np.asarray(y_true, dtype=np.int64)
self.y_true = _coerce_y_true(y_true)
self.split = np.full((n_folds, len(entity_ids)), "", dtype=object)
self._sid = {str(eid): i for i, eid in enumerate(entity_ids)}
# head_name → (buffer array, n_epochs)
@@ -148,7 +160,7 @@ class PredictionStore:
"""Register a training phase before recording begins."""
self._phases[phase] = _PhaseBuffer(
entity_ids=list(entity_ids),
y_true=np.asarray(y_true, dtype=np.int64),
y_true=_coerce_y_true(y_true),
head_names=list(head_names),
n_epochs=n_epochs,
n_folds=self.n_folds,
@@ -306,7 +318,7 @@ class FeatureStore:
) -> None:
self._phases[phase] = _FeaturePhaseBuffer(
entity_ids=list(entity_ids),
y_true=np.asarray(y_true, dtype=np.int64),
y_true=_coerce_y_true(y_true),
n_folds=self.n_folds,
)