From 17ae7a49bd18696d39d43e4e305a23495494a61f Mon Sep 17 00:00:00 2001 From: Sezgin Er Date: Mon, 15 Jun 2026 21:12:56 +0200 Subject: [PATCH] Add 14-class merged-group labels and no-re-extract relabel tool for linear probing - scripts/eval_labels/splits_merged_majority/: the 14 clinical-group labels for the probe. Each of 37 pathologies is set by a 3-model majority vote (Claude Opus 4.7 + GPT-5.5 + Nemotron-3 Super 120B), then collapsed into the neuroradiologist's 8 pathophysiology (PP_*) + 6 imaging-phenotype (BP_*) groups. Reproducible via build_merged_group_labels.py --source majority (the builder also supports raw/csv32 agreement variants on demand). - scripts/relabel_features.py: build a linear-probe feature dir for a different labels CSV by reusing already-cached encoder features (symlink features_*.npy / subject_ids_*.txt, rebuild only labels_*.npy + label_names.json). Avoids re-running the frozen encoder when only labels change; errors out if a cached subject is missing from the new labels CSV. - README: Linear Probing section documents the label set (class count is derived from the CSV, no code change) and the extract-once / relabel workflow. Bulky derived CSVs (all/train/val/test) are gitignored. --- contrastive-pretraining/README.md | 46 +- .../scripts/eval_labels/.gitignore | 7 + .../eval_labels/build_merged_group_labels.py | 296 + .../scripts/eval_labels/build_presentation.py | 263 + .../merged_labels_presentation.html | 396 + .../splits_merged_majority/README.md | 57 + .../group_definitions.json | 89 + .../mrrate_merged_labels.csv | 97897 ++++++++++++++++ .../splits_merged_majority/pathologies.json | 60 + .../splits_merged_majority/splits.csv | 97897 ++++++++++++++++ .../submit_linear_probe.sh | 55 + .../scripts/relabel_features.py | 118 + 12 files changed, 197178 insertions(+), 3 deletions(-) create mode 100644 contrastive-pretraining/scripts/eval_labels/.gitignore create mode 100644 contrastive-pretraining/scripts/eval_labels/build_merged_group_labels.py create mode 100644 contrastive-pretraining/scripts/eval_labels/build_presentation.py create mode 100644 contrastive-pretraining/scripts/eval_labels/merged_labels_presentation.html create mode 100644 contrastive-pretraining/scripts/eval_labels/splits_merged_majority/README.md create mode 100644 contrastive-pretraining/scripts/eval_labels/splits_merged_majority/group_definitions.json create mode 100644 contrastive-pretraining/scripts/eval_labels/splits_merged_majority/mrrate_merged_labels.csv create mode 100644 contrastive-pretraining/scripts/eval_labels/splits_merged_majority/pathologies.json create mode 100644 contrastive-pretraining/scripts/eval_labels/splits_merged_majority/splits.csv create mode 100755 contrastive-pretraining/scripts/eval_labels/splits_merged_majority/submit_linear_probe.sh create mode 100644 contrastive-pretraining/scripts/relabel_features.py diff --git a/contrastive-pretraining/README.md b/contrastive-pretraining/README.md index b0575c8..ba694d5 100644 --- a/contrastive-pretraining/README.md +++ b/contrastive-pretraining/README.md @@ -527,12 +527,23 @@ Column names must match the pathology labels in `--pathologies_file`. The pre-co A supervised baseline that freezes the pretrained MR-RATE encoder and trains only a linear classifier on top of its pooled visual features. This is the standard probe used to measure how well the contrastive representation captures the downstream pathology labels, separate from the zero-shot prompt quality. -The probe is two scripts run in order: +The probe is two (optionally three) scripts run in order: 1. **`extract_features.py`** — encode every subject once with the frozen encoder and dump features to disk. This is the slow step; it runs the same model + preprocessing path as `inference.py`. Run it once per split. 2. **`linear_probe.py`** — train `nn.Linear(dim_latent, num_classes)` with `BCEWithLogitsLoss` on the cached features, pick the best epoch by val mean-AUROC, report test metrics through `eval.evaluate_internal` (identical AUROC pipeline to inference). +3. **`relabel_features.py`** *(optional)* — swap in a different label set on top of already-cached features **without re-encoding**. See [Reusing features for a different label set](#reusing-features-for-a-different-label-set-no-re-extract). -Why precompute features: the 3D encoder is the expensive part. Once it is frozen, every training epoch sees the same features, so running the encoder once and then training the linear head on cached `.npy` files is orders of magnitude faster than re-encoding each epoch — the standard CLIP / SimCLR / DINO linear-probe recipe. +Why precompute features: the 3D encoder is the expensive part. Once it is frozen, every training epoch sees the same features, so running the encoder once and then training the linear head on cached `.npy` files is orders of magnitude faster than re-encoding each epoch — the standard CLIP / SimCLR / DINO linear-probe recipe. The corollary: **the labels are not part of the features**, so changing the label set never requires re-extraction — only the cheap `linear_probe.py` step is repeated (see step 3). + +### Label sets + +The class count is **derived from the labels CSV at runtime** — `extract_features.py` sets `num_classes = len(label_columns)` and writes `label_names.json` from the CSV header, and `linear_probe.py` sizes `nn.Linear(dim, n_classes)` from that. Nothing is hardcoded, so any labels CSV (`study_uid` + binary class columns) is a drop-in `--labels_file` with no code change. The ready-made set lives under `scripts/eval_labels/`: + +| Folder | Classes | Ground-truth rule | +|--------|--------:|-------------------| +| `splits_merged_majority/` (`mrrate_merged_labels.csv`) | 14 | 3-model majority (Claude Opus 4.7 + GPT-5.5 + Nemotron-3 Super 120B; positive when ≥2 of the available votes agree), then collapsed into the neuroradiologist's clinical groups (8 pathophysiology `PP_*` + 6 imaging-phenotype `BP_*`) | + +It pairs a labels CSV (`study_uid` + 14 binary class columns) with a `splits.csv` (`study_uid,split`). Reproduce it with `scripts/eval_labels/build_merged_group_labels.py --source majority` (the script also supports `--source {raw,csv32}` for 2-model agreement variants if you need them). ### Step 1 — Cache features @@ -590,9 +601,38 @@ Outputs in `--results_dir`: The best epoch by val mean-AUROC is restored before test evaluation. Single-class columns (all 0 or all 1 in the test split) are gracefully reported as `NaN` and excluded from the macro mean. +### Reusing features for a different label set (no re-extract) + +Because labels are not baked into the encoder features, you only ever run `extract_features.py` **once**. To probe a different label CSV — a different ground-truth rule, or a different grouping — relabel the cached features instead of re-encoding: + +```bash +# (once) cache features +for SPLIT in train val test; do + python scripts/extract_features.py \ + --encoder vjepa2 --fusion_mode late --pooling_strategy simple_attn \ + --weights_path ./mr_rate_results/MrRate.5000.pt \ + --data_folder /path/to/data --jsonl_file /path/to/reports.jsonl \ + --labels_file scripts/eval_labels/splits_merged_majority/mrrate_merged_labels.csv \ + --splits_csv scripts/eval_labels/splits_merged_majority/splits.csv \ + --split $SPLIT --normalizer zscore --out_dir ./lp_features +done + +# train the head on those features +python scripts/linear_probe.py --features_dir ./lp_features --results_dir ./lp_results + +# later: probe ANY other labels CSV on the SAME features — instant, no encoder pass +python scripts/relabel_features.py \ + --features_dir ./lp_features \ + --labels_file /path/to/other_labels.csv \ + --out_dir ./lp_features_other +python scripts/linear_probe.py --features_dir ./lp_features_other --results_dir ./lp_results_other +``` + +`relabel_features.py` symlinks `features_.npy` + `subject_ids_.txt` from `--features_dir` and rebuilds only `labels_.npy` (in the exact subject order of the source) and `label_names.json` from `--labels_file`. If a cached subject is missing from the new labels CSV it **errors out** rather than silently misaligning rows. Use `--copy` to copy the feature files instead of symlinking (e.g. to move the dir to another machine). + ### Notes -- Both scripts are single-process. The linear head is `Linear(512, 32) ≈ 16K params` over ~180 MB of cached features — DDP overhead would dominate. For feature extraction, shard externally by running one job per split, or by splitting `splits.csv` into chunks and concatenating the resulting `.npy` files. +- Both scripts are single-process. The linear head is tiny — `Linear(512, num_classes)`, e.g. ~16K params for 32 classes or ~7K for the 14 merged groups — over ~180 MB of cached features, so DDP overhead would dominate. For feature extraction, shard externally by running one job per split, or by splitting `splits.csv` into chunks and concatenating the resulting `.npy` files. - `--encoder`, `--fusion_mode`, `--pooling_strategy`, and `--dim_latent` must match the values used when the checkpoint was trained; if they don't, `_load_and_verify` will surface it loudly instead of silently loading garbage. ## Testing diff --git a/contrastive-pretraining/scripts/eval_labels/.gitignore b/contrastive-pretraining/scripts/eval_labels/.gitignore new file mode 100644 index 0000000..e4b15de --- /dev/null +++ b/contrastive-pretraining/scripts/eval_labels/.gitignore @@ -0,0 +1,7 @@ +# Large derived / convenience CSVs — the linear probe only consumes +# mrrate_*labels.csv + splits.csv. These are regenerable with +# build_merged_group_labels.py, so they are kept out of git to avoid bloat. +**/all.csv +**/train.csv +**/val.csv +**/test.csv diff --git a/contrastive-pretraining/scripts/eval_labels/build_merged_group_labels.py b/contrastive-pretraining/scripts/eval_labels/build_merged_group_labels.py new file mode 100644 index 0000000..dcab73f --- /dev/null +++ b/contrastive-pretraining/scripts/eval_labels/build_merged_group_labels.py @@ -0,0 +1,296 @@ +"""Build the neuroradiologist's merged-group labels in a SINGLE combined CSV, +recomputing the Opus(Claude) AND GPT(OpenAI) agreement from the RAW prediction +JSONs over ALL 37 pathologies (so the 5 pathologies dropped in +build_agreement_splits.py are recovered, not ignored). + +Two grouping schemes (from Bene, neuroradiology): + 1) Pathophysiologie -> 8 groups, columns prefixed "PP_" + 2) Bildphaenotyp -> 6 groups, columns prefixed "BP_" + +A study is positive for a group iff it is positive for ANY member pathology +(logical OR). Per-pathology agreement uses the same strict AND rule as +build_agreement_splits.py: + both 1 -> 1 | any 0 -> 0 | one side missing -> present side | both missing -> 0(blank) + +The two pathologies ungrouped in BOTH schemes (Empty sella syndrome, +Hyperostosis of skull) are intentionally excluded. + +Inputs (same sources as build_agreement_splits.py, but NOTHING is dropped): + eval_set_predictions_5k.json -> claude_labels + gpt_labels + ../../../remaining_eval/eval_set_predictions_chunk_0{0..4}.json + -> nvidia_opus47_labels + nvidia_gpt55_labels + splits_hf.csv -> study_uid -> split + +Outputs (under splits_merged/): + mrrate_merged_labels.csv -- study_uid + 14 binary group cols + pathologies.json -- same 14 names, ready for --pathologies_file + group_definitions.json -- group -> members (all present now) + splits.csv -- batch_id,patient_uid,study_uid,split (kept UIDs only) +""" +from __future__ import annotations + +import argparse +import csv +import json +from pathlib import Path + +HERE = Path(__file__).parent +PRED_5K = HERE / "eval_set_predictions_5k.json" +CHUNK_DIR = Path("/hnvme/workspace/b180dc51-sezgin/MR-RATE/remaining_eval") +# 92k chunks augmented with a 3rd model (Nemotron) for the "majority" mode. +CHUNK_DIR_NEM = Path("/hnvme/workspace/b180dc51-sezgin/remaining_eval_with_nemotron-2") +SPLITS_CSV = HERE / "splits_hf.csv" +# 32-column agreement labels (5 pathologies dropped) for the legacy "csv32" mode. +CSV32_LABELS = HERE / "splits_agreement" / "mrrate_labels.csv" +CSV32_SPLITS = HERE / "splits_agreement" / "splits.csv" + +PP_GROUPS = [ + ("PP_Cerebrovascular", ["Cerebral infarction", "Cerebral hemorrhage", "Lacunar infarct", + "Silent micro-hemorrhage of brain", "Cavernous hemangioma", + "Subdural intracranial hemorrhage", "Intracranial aneurysm", "Watershed infarct"]), + ("PP_Neoplastic", ["Metastatic malignant neoplasm to brain", "Intracranial meningioma", + "Glioma", "Pituitary adenoma", "Schwannoma"]), + ("PP_Neurodegenerative", ["Cerebral atrophy", "Ventriculomegaly", "Cerebellar degeneration"]), + ("PP_Spinal", ["Herniation of nucleus pulposus", "Spinal cord compression", + "Foraminal Spinal Stenosis", "Spinal stenosis", "Hemangioma of vertebral column"]), + ("PP_Cystic_developmental", ["Arachnoid cyst", "Cyst of pineal gland", + "Structure of cave of septum pellucidum", "Mega cisterna magna", "Chiari malformation", + "Rathke's pouch cyst", "Choroid plexus cyst", "Lipoma of brain"]), + ("PP_Infectious", ["Mastoiditis", "Chronic mastoiditis"]), + ("PP_Inflammatory", ["Demyelinating disease of central nervous system"]), + ("PP_Unspecific_bucket", ["Gliosis", "Cerebral edema", "Encephalomalacia"]), +] + +BP_GROUPS = [ + ("BP_Atrophies", ["Cerebral atrophy", "Ventriculomegaly", "Cerebellar degeneration"]), + ("BP_Contrast_enhancing_intracranial", ["Metastatic malignant neoplasm to brain", + "Intracranial meningioma", "Glioma", "Pituitary adenoma", "Schwannoma"]), + ("BP_Infectious_lesions", ["Mastoiditis", "Chronic mastoiditis"]), + ("BP_Edematous_lesions", ["Cerebral infarction", "Lacunar infarct", "Watershed infarct", + "Demyelinating disease of central nervous system"]), + ("BP_Hemorrhagic_lesions", ["Cerebral hemorrhage", "Silent micro-hemorrhage of brain", + "Cavernous hemangioma", "Encephalomalacia"]), + ("BP_Cystic_lesions", ["Arachnoid cyst", "Cyst of pineal gland", + "Structure of cave of septum pellucidum", "Mega cisterna magna", + "Rathke's pouch cyst", "Choroid plexus cyst"]), +] + +ALL_GROUPS = PP_GROUPS + BP_GROUPS + + +def agreement(a: dict | None, b: dict | None, p: str) -> int: + """Strict AND per pathology. Returns 1 (agree positive) else 0. + + both1 -> 1 | any0 -> 0 | one missing -> present side | both missing -> 0. + """ + a = a or {} + b = b or {} + x, y = a.get(p), b.get(p) + if x is None and y is None: + return 0 + if x is None: + return 1 if y == 1 else 0 + if y is None: + return 1 if x == 1 else 0 + return 1 if (x == 1 and y == 1) else 0 + + +def load_split_map(path: Path) -> dict[str, dict]: + m: dict[str, dict] = {} + with path.open() as f: + for r in csv.DictReader(f): + m[r["study_uid"]] = { + "split": r["split"], + "batch_id": r.get("batch_id", ""), + "patient_uid": r.get("patient_uid", ""), + } + return m + + +def write_outputs(out_dir: Path, rows: list[tuple[str, list[int]]], split_map: dict) -> None: + """rows = [(study_uid, [group values...])]; writes the 4 artifacts.""" + out_dir.mkdir(exist_ok=True) + out_cols = [name for name, _ in ALL_GROUPS] + counts = {c: 0 for c in out_cols} + kept_uids: set[str] = set() + n_split = {"train": 0, "val": 0, "test": 0} + + out_path = out_dir / "mrrate_merged_labels.csv" + per_split: dict[str, list[list]] = {"train": [], "val": [], "test": []} + with open(out_path, "w", newline="") as f: + w = csv.writer(f) + w.writerow(["study_uid"] + out_cols) + for uid, vals in rows: + w.writerow([uid] + vals) + kept_uids.add(uid) + split = split_map[uid]["split"] + n_split[split] += 1 + per_split[split].append([uid] + vals) + for name, v in zip(out_cols, vals): + counts[name] += v + + # Per-split CSVs (train.csv / val.csv / test.csv), same columns as the + # combined labels file. + for split, srows in per_split.items(): + fname = "val.csv" if split == "val" else f"{split}.csv" + with open(out_dir / fname, "w", newline="") as f: + w = csv.writer(f) + w.writerow(["study_uid"] + out_cols) + w.writerows(srows) + + paths_json = {} + for name, _ in ALL_GROUPS: + label = name.replace("PP_", "").replace("BP_", "").replace("_", " ") + paths_json[name] = {"positive": f"There is {label}", "negative": f"There is no {label}"} + (out_dir / "pathologies.json").write_text(json.dumps({"pathologies": paths_json}, indent=2)) + (out_dir / "group_definitions.json").write_text( + json.dumps({name: ms for name, ms in ALL_GROUPS}, indent=2) + ) + with open(out_dir / "splits.csv", "w", newline="") as f: + w = csv.writer(f) + w.writerow(["batch_id", "patient_uid", "study_uid", "split"]) + for uid, meta in split_map.items(): + if uid in kept_uids: + w.writerow([meta["batch_id"], meta["patient_uid"], uid, meta["split"]]) + + n = len(kept_uids) + print(f"\nWrote {out_path} ({n:,} studies, {len(out_cols)} label columns)") + print(f" kept per split: {n_split}") + print(f"{'column':40s} {'pos':>7s} {'%':>6s}") + for name, _ in ALL_GROUPS: + print(f"{name:40s} {counts[name]:7d} {100*counts[name]/n:5.1f}") + + +def build_from_csv32(out_dir: Path) -> None: + """Legacy mode: OR-collapse the existing 32-column agreement labels. + The 5 dropped pathologies are absent and contribute nothing.""" + split_map = load_split_map(CSV32_SPLITS) + with open(CSV32_LABELS) as f: + reader = csv.DictReader(f) + present = set(reader.fieldnames) - {"study_uid"} + rows = [] + for row in reader: + uid = row["study_uid"] + if uid not in split_map: + continue + vals = [] + for _, ms in ALL_GROUPS: + used = [m for m in ms if m in present] + vals.append(1 if any(float(row[m]) > 0 for m in used) else 0) + rows.append((uid, vals)) + write_outputs(out_dir, rows, split_map) + + +def build_from_raw(out_dir: Path) -> None: + split_map = load_split_map(SPLITS_CSV) + + pred_5k = json.loads(PRED_5K.read_text()) + paths_all = pred_5k["pathologies"] # 37, in canonical order + + # Validate every group member is a real pathology. + members = {m for _, ms in ALL_GROUPS for m in ms} + unknown = members - set(paths_all) + if unknown: + raise RuntimeError(f"Unknown group members: {sorted(unknown)}") + + # study_uid -> (claude/opus labels, gpt labels). 5k wins over chunks. + pred: dict[str, tuple[dict, dict]] = {} + for it in pred_5k["items"]: + pred[it["study_uid"]] = (it.get("claude_labels") or {}, it.get("gpt_labels") or {}) + print(f"loaded 5k preds: {len(pred):,}") + + for i in range(5): + chunk = json.loads((CHUNK_DIR / f"eval_set_predictions_chunk_0{i}.json").read_text()) + assert chunk["pathologies"] == paths_all, f"pathology mismatch in chunk {i}" + for it in chunk["items"]: + uid = it["study_uid"] + if uid in pred: + continue + pred[uid] = (it.get("nvidia_opus47_labels") or {}, it.get("nvidia_gpt55_labels") or {}) + print(f"loaded chunk_0{i}: cumulative {len(pred):,}") + + rows = [] + no_pred = {"train": 0, "val": 0, "test": 0} + for uid, meta in split_map.items(): + if uid not in pred: + no_pred[meta["split"]] += 1 + continue + a, b = pred[uid] + agree = {p: agreement(a, b, p) for p in members} + vals = [1 if any(agree[m] for m in ms) else 0 for _, ms in ALL_GROUPS] + rows.append((uid, vals)) + print(f"no-pred skipped: {no_pred}") + write_outputs(out_dir, rows, split_map) + + +def majority(votes: list, p: str) -> int: + """Strict majority of PRESENT votes for pathology p. + pos*2 > present -> 1. 3 present -> need 2; 2 present -> need both; 1 -> need it.""" + present = [d.get(p) for d in votes if (d or {}).get(p) is not None] + if not present: + return 0 + pos = sum(1 for v in present if v == 1) + return 1 if pos * 2 > len(present) else 0 + + +def build_majority(out_dir: Path) -> None: + """3-way majority of Claude(Opus) + GPT + Nemotron, then OR into groups. + 5k uses claude/gpt/nemotron; 92k uses the nemotron-augmented chunks.""" + split_map = load_split_map(SPLITS_CSV) + pred_5k = json.loads(PRED_5K.read_text()) + paths_all = pred_5k["pathologies"] + members = {m for _, ms in ALL_GROUPS for m in ms} + + # study_uid -> (claude/opus, gpt, nemotron). 5k wins over chunks. + pred: dict[str, tuple[dict, dict, dict]] = {} + n_models = {0: 0, 1: 0, 2: 0, 3: 0} + for it in pred_5k["items"]: + pred[it["study_uid"]] = ( + it.get("claude_labels") or {}, + it.get("gpt_labels") or {}, + it.get("nemotron_labels") or {}, + ) + print(f"loaded 5k preds: {len(pred):,}") + + for i in range(5): + chunk = json.loads((CHUNK_DIR_NEM / f"eval_set_predictions_chunk_0{i}.json").read_text()) + assert chunk["pathologies"] == paths_all, f"pathology mismatch in chunk {i}" + for it in chunk["items"]: + uid = it["study_uid"] + if uid in pred: + continue + pred[uid] = ( + it.get("nvidia_opus47_labels") or {}, + it.get("nvidia_gpt55_labels") or {}, + it.get("nvidia_nemotron3_super_v3_labels") or {}, + ) + print(f"loaded chunk_0{i}: cumulative {len(pred):,}") + + rows = [] + no_pred = {"train": 0, "val": 0, "test": 0} + for uid, meta in split_map.items(): + if uid not in pred: + no_pred[meta["split"]] += 1 + continue + votes = pred[uid] + n_models[sum(1 for d in votes if d)] += 1 + maj = {p: majority(votes, p) for p in members} + vals = [1 if any(maj[m] for m in ms) else 0 for _, ms in ALL_GROUPS] + rows.append((uid, vals)) + print(f"no-pred skipped: {no_pred}") + print(f"models available per study: {n_models} (3 = full 3-way majority)") + write_outputs(out_dir, rows, split_map) + + +if __name__ == "__main__": + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--source", choices=["raw", "csv32", "majority"], default="raw", + help="raw = Claude AND GPT agreement over all 37 pathologies (default); " + "csv32 = collapse the existing 32-col labels (5 paths stay dropped); " + "majority = 3-way majority of Claude + GPT + Nemotron.") + ap.add_argument("--out-dir", default=None, help="output folder (default per source).") + a = ap.parse_args() + defaults = {"raw": "splits_merged", "csv32": "splits_merged_32col", + "majority": "splits_merged_majority"} + out = Path(a.out_dir) if a.out_dir else HERE / defaults[a.source] + {"raw": build_from_raw, "csv32": build_from_csv32, "majority": build_majority}[a.source](out) diff --git a/contrastive-pretraining/scripts/eval_labels/build_presentation.py b/contrastive-pretraining/scripts/eval_labels/build_presentation.py new file mode 100644 index 0000000..c8db116 --- /dev/null +++ b/contrastive-pretraining/scripts/eval_labels/build_presentation.py @@ -0,0 +1,263 @@ +"""Generate a self-contained HTML presentation of the merged-group labels. + +Reads the produced splits_merged / splits_merged_32col folders and emits +merged_labels_presentation.html (inline CSS, no external dependencies). +""" +from __future__ import annotations + +import csv +import json +from pathlib import Path + +HERE = Path(__file__).parent +RAW = HERE / "splits_merged_majority" # primary: 3-way majority +LEGACY = HERE / "splits_merged" # baseline: 2-model Claude AND GPT +OUT = HERE / "merged_labels_presentation.html" + +# No per-column "recovered" badges in the majority story. +RECOVERED = {} + +SCHEMES = { + "Pathophysiologie": ("PP_", "#2563eb"), + "Bildphänotyp": ("BP_", "#9333ea"), +} + + +def load(folder: Path): + cols = next(csv.reader(open(folder / "mrrate_merged_labels.csv")))[1:] + defs = json.loads((folder / "group_definitions.json").read_text()) + split = {r["study_uid"]: r["split"] for r in csv.DictReader(open(folder / "splits.csv"))} + pos = {c: {"train": 0, "val": 0, "test": 0, "all": 0} for c in cols} + n = {"train": 0, "val": 0, "test": 0, "all": 0} + for row in csv.DictReader(open(folder / "mrrate_merged_labels.csv")): + s = split[row["study_uid"]] + n[s] += 1 + n["all"] += 1 + for c in cols: + if row[c] == "1": + pos[c][s] += 1 + pos[c]["all"] += 1 + return cols, defs, pos, n + + +def pct(a, b): + return 100 * a / b if b else 0 + + +def esc(s): + return s.replace("&", "&").replace("<", "<").replace(">", ">") + + +def main() -> None: + cols, defs, pos, n = load(RAW) + _, _, lpos, _ = load(LEGACY) + + label = lambda c: c.replace("PP_", "").replace("BP_", "").replace("_", " ") + scheme_of = lambda c: "Pathophysiologie" if c.startswith("PP_") else "Bildphänotyp" + color_of = lambda c: SCHEMES[scheme_of(c)][1] + maxpct = max(pct(pos[c]["all"], n["all"]) for c in cols) + + # ---- group cards ---- + def group_card(c): + col = color_of(c) + members = defs[c] + chips = "".join( + f'{esc(m)}' + for m in members + ) + rec = RECOVERED.get(c, []) + rec_html = "" + if rec: + rec_html = ( + '
recovered: ' + + ", ".join(esc(m) for m in rec) + + "
" + ) + p = pct(pos[c]["all"], n["all"]) + return f""" +
+
+
{esc(c)}
+
{pos[c]['all']:,} ({p:.1f}%)
+
+
+
{chips}
+ {rec_html} +
""" + + pp_cards = "".join(group_card(c) for c in cols if c.startswith("PP_")) + bp_cards = "".join(group_card(c) for c in cols if c.startswith("BP_")) + + # ---- prevalence table ---- + rows_html = "" + for c in cols: + col = color_of(c) + diff = pos[c]["all"] - lpos[c]["all"] + if diff > 0: + diff_html = f'+{diff:,}' + elif diff < 0: + diff_html = f'{diff:,}' + else: + diff_html = '—' + rows_html += f""" + + {esc(c)} + {pos[c]['train']:,} + {pos[c]['val']:,} + {pos[c]['test']:,} + {pos[c]['all']:,} + {pct(pos[c]['all'], n['all']):.1f}% + {diff_html} + """ + + html = f""" + + +MR-RATE · Merged Pathology Group Labels + + + +
+
MR-RATE · linear-probe targets
+

Merged Pathology Group Labels

+

Radiology-report findings collapsed from 37 fine-grained pathologies into the + neuroradiologist's clinically-grounded groups, derived from a 3-model majority vote.

+
+
{n['all']:,}studies labeled
+
{len(cols)}group targets
+
3voting models
+
{n['train']:,} / {n['val']:,} / {n['test']:,}train / val / test
+
+
+ +
+ +
+

How the ground-truth labels are derived

+

Each study is independently labeled by three models; a finding is positive when a majority agree (≥2 of the available votes). Groups are then the logical OR of their member pathologies.

+
+
Anthropic
Claude Opus 4.7
+
OpenAI
GPT-5.5
+
NVIDIA
Nemotron-3 Super 120B
+
→
+
Majority vote
37 pathology labels
+
→
+
OR per group
{len(cols)} merged targets
+
+
+ +
+

Grouping scheme 1 — Pathophysiologie

+

Eight mutually-exclusive groups by underlying mechanism. Chips list member pathologies.

+
{pp_cards}
+
+ +
+

Grouping scheme 2 — Bildphänotyp

+

Six groups by imaging phenotype. (Three further phenotype groups are identical to their pathophysiology counterparts and are not duplicated.)

+
{bp_cards}
+
+ +
+

Prevalence by split

+

Positive study counts per group. The last column shows the change versus the 2-model (Claude ∧ GPT) agreement labels.

+ + + + + {rows_html} +
Grouptrain+val+test+total+prev.Δ vs 2-model
+
+ +
+

Label variants available

+

Same 14 columns and study set throughout — only the voting rule differs.

+
+ splits_merged_majority/  — this report. 3-way majority of Claude Opus 4.7 + GPT-5.5 + Nemotron-3 Super 120B (≥2 of available votes), over all 37 pathologies.

+ splits_merged/  — 2-model strict agreement (Claude ∧ GPT).

+ splits_merged_32col/  — legacy 2-model agreement built from the 32-column labels (5 pathologies dropped). +
+
+ +
+

Files & usage

+
+
mrrate_merged_labels.csvstudy_uid + {len(cols)} binary columns
+
train.csv / val.csv / test.csvper-split, same columns
+
splits.csvstudy_uid → split
+
pathologies.jsongroup names & prompts
+
group_definitions.jsongroup → member pathologies
+
+ 
--labels_csv  splits_merged/mrrate_merged_labels.csv
+--splits_csv  splits_merged/splits.csv
+--split       train        # / val / test
+
+ +
MR-RATE · merged-group labels for linear probing · 3-model majority: Claude Opus 4.7 · GPT-5.5 · Nemotron-3 Super 120B
+
+""" + + OUT.write_text(html) + print(f"wrote {OUT} ({OUT.stat().st_size/1024:.0f} KB)") + + +if __name__ == "__main__": + main() diff --git a/contrastive-pretraining/scripts/eval_labels/merged_labels_presentation.html b/contrastive-pretraining/scripts/eval_labels/merged_labels_presentation.html new file mode 100644 index 0000000..eb12afa --- /dev/null +++ b/contrastive-pretraining/scripts/eval_labels/merged_labels_presentation.html @@ -0,0 +1,396 @@ + + + +MR-RATE · Merged Pathology Group Labels + + + +
+
MR-RATE · linear-probe targets
+

Merged Pathology Group Labels

+

Radiology-report findings collapsed from 37 fine-grained pathologies into the + neuroradiologist's clinically-grounded groups, derived from a 3-model majority vote.

+
+
97,896studies labeled
+
14group targets
+
3voting models
+
88,582 / 3,764 / 5,550train / val / test
+
+
+ +
+ +
+

How the ground-truth labels are derived

+

Each study is independently labeled by three models; a finding is positive when a majority agree (≥2 of the available votes). Groups are then the logical OR of their member pathologies.

+
+
Anthropic
Claude Opus 4.7
+
OpenAI
GPT-5.5
+
NVIDIA
Nemotron-3 Super 120B
+
→
+
Majority vote
37 pathology labels
+
→
+
OR per group
14 merged targets
+
+
+ +
+

Grouping scheme 1 — Pathophysiologie

+

Eight mutually-exclusive groups by underlying mechanism. Chips list member pathologies.

+
+
+
+
PP_Cerebrovascular
+
9,847 (10.1%)
+
+
+
Cerebral infarctionCerebral hemorrhageLacunar infarctSilent micro-hemorrhage of brainCavernous hemangiomaSubdural intracranial hemorrhageIntracranial aneurysmWatershed infarct
+ +
+
+
+
PP_Neoplastic
+
8,112 (8.3%)
+
+
+
Metastatic malignant neoplasm to brainIntracranial meningiomaGliomaPituitary adenomaSchwannoma
+ +
+
+
+
PP_Neurodegenerative
+
18,237 (18.6%)
+
+
+
Cerebral atrophyVentriculomegalyCerebellar degeneration
+ +
+
+
+
PP_Spinal
+
1,955 (2.0%)
+
+
+
Herniation of nucleus pulposusSpinal cord compressionForaminal Spinal StenosisSpinal stenosisHemangioma of vertebral column
+ +
+
+
+
PP_Cystic_developmental
+
9,216 (9.4%)
+
+
+
Arachnoid cystCyst of pineal glandStructure of cave of septum pellucidumMega cisterna magnaChiari malformationRathke's pouch cystChoroid plexus cystLipoma of brain
+ +
+
+
+
PP_Infectious
+
4,779 (4.9%)
+
+
+
MastoiditisChronic mastoiditis
+ +
+
+
+
PP_Inflammatory
+
2,882 (2.9%)
+
+
+
Demyelinating disease of central nervous system
+ +
+
+
+
PP_Unspecific_bucket
+
40,483 (41.4%)
+
+
+
GliosisCerebral edemaEncephalomalacia
+ +
+
+ +
+

Grouping scheme 2 — Bildphänotyp

+

Six groups by imaging phenotype. (Three further phenotype groups are identical to their pathophysiology counterparts and are not duplicated.)

+
+
+
+
BP_Atrophies
+
18,237 (18.6%)
+
+
+
Cerebral atrophyVentriculomegalyCerebellar degeneration
+ +
+
+
+
BP_Contrast_enhancing_intracranial
+
8,112 (8.3%)
+
+
+
Metastatic malignant neoplasm to brainIntracranial meningiomaGliomaPituitary adenomaSchwannoma
+ +
+
+
+
BP_Infectious_lesions
+
4,779 (4.9%)
+
+
+
MastoiditisChronic mastoiditis
+ +
+
+
+
BP_Edematous_lesions
+
8,279 (8.5%)
+
+
+
Cerebral infarctionLacunar infarctWatershed infarctDemyelinating disease of central nervous system
+ +
+
+
+
BP_Hemorrhagic_lesions
+
7,413 (7.6%)
+
+
+
Cerebral hemorrhageSilent micro-hemorrhage of brainCavernous hemangiomaEncephalomalacia
+ +
+
+
+
BP_Cystic_lesions
+
8,568 (8.8%)
+
+
+
Arachnoid cystCyst of pineal glandStructure of cave of septum pellucidumMega cisterna magnaRathke's pouch cystChoroid plexus cyst
+ +
+
+ +
+

Prevalence by split

+

Positive study counts per group. The last column shows the change versus the 2-model (Claude ∧ GPT) agreement labels.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Grouptrain+val+test+total+prev.Δ vs 2-model
PP_Cerebrovascular8,9163455869,84710.1%+919
PP_Neoplastic7,3822924388,1128.3%+102
PP_Neurodegenerative16,5956001,04218,23718.6%+561
PP_Spinal1,81451901,9552.0%+19
PP_Cystic_developmental8,3533555089,2169.4%+127
PP_Infectious4,3491782524,7794.9%+424
PP_Inflammatory2,72171902,8822.9%+38
PP_Unspecific_bucket36,6921,4872,30440,48341.4%+462
BP_Atrophies16,5956001,04218,23718.6%+561
BP_Contrast_enhancing_intracranial7,3822924388,1128.3%+102
BP_Infectious_lesions4,3491782524,7794.9%+424
BP_Edematous_lesions7,5992654158,2798.5%+166
BP_Hemorrhagic_lesions6,7452494197,4137.6%+839
BP_Cystic_lesions7,7683274738,5688.8%+58
+
+ +
+

Label variants available

+

Same 14 columns and study set throughout — only the voting rule differs.

+
+ splits_merged_majority/  — this report. 3-way majority of Claude Opus 4.7 + GPT-5.5 + Nemotron-3 Super 120B (≥2 of available votes), over all 37 pathologies.

+ splits_merged/  — 2-model strict agreement (Claude ∧ GPT).

+ splits_merged_32col/  — legacy 2-model agreement built from the 32-column labels (5 pathologies dropped). +
+
+ +
+

Files & usage

+
+
mrrate_merged_labels.csvstudy_uid + 14 binary columns
+
train.csv / val.csv / test.csvper-split, same columns
+
splits.csvstudy_uid → split
+
pathologies.jsongroup names & prompts
+
group_definitions.jsongroup → member pathologies
+
+ 
--labels_csv  splits_merged/mrrate_merged_labels.csv
+--splits_csv  splits_merged/splits.csv
+--split       train        # / val / test
+
+ +
MR-RATE · merged-group labels for linear probing · 3-model majority: Claude Opus 4.7 · GPT-5.5 · Nemotron-3 Super 120B
+
+ \ No newline at end of file diff --git a/contrastive-pretraining/scripts/eval_labels/splits_merged_majority/README.md b/contrastive-pretraining/scripts/eval_labels/splits_merged_majority/README.md new file mode 100644 index 0000000..63b1bfd --- /dev/null +++ b/contrastive-pretraining/scripts/eval_labels/splits_merged_majority/README.md @@ -0,0 +1,57 @@ +# splits_merged_majority — linear-probe labels (14 merged groups) + +Ground-truth labels for the MR-RATE linear probe, built from a **3-model +majority vote** (Claude Opus 4.7 · GPT-5.5 · Nemotron-3 Super 120B; positive +when ≥2 of the available votes agree) over 37 pathologies, then collapsed into +the neuroradiologist's **14 clinical groups** (8 Pathophysiologie `PP_*` + +6 Bildphänotyp `BP_*`). + +## Files the probe consumes + +| File | Used by | Format | +|------|---------|--------| +| `mrrate_merged_labels.csv` | `extract_features.py --labels_file` | `study_uid` + 14 binary class columns | +| `splits.csv` | `extract_features.py --splits_csv` | `…,study_uid,split` (train/val/test) | + +Both match the `data_inference.MRReportDatasetInfer` loader contract verbatim: +`_load_labels` keys on `study_uid` and treats every other column as a class; +`_load_splits` filters on `row['split']`. The class count (14) is derived +automatically — nothing is hardcoded — so `label_names.json` is written by +`extract_features.py` straight from these column names. + +Coverage: 97,896 studies — train 88,582 / val 3,764 / test 5,550. + +The other files here are **not** needed by the probe: +`train.csv`/`val.csv`/`test.csv` (per-split convenience copies), +`pathologies.json` (zero-shot prompts, for `inference.py`), +`group_definitions.json` (group → member pathologies, documentation). + +## Run (two steps) + +```bash +cd contrastive-pretraining +LAB=scripts/eval_labels/splits_merged_majority + +# 1) cache frozen-encoder features once per split +for SPLIT in train val test; do + python scripts/extract_features.py \ + --encoder vjepa2 --fusion_mode late --pooling_strategy simple_attn \ + --weights_path ./mr_rate_results/MrRate.5000.pt \ + --data_folder /path/to/data \ + --jsonl_file /path/to/reports.jsonl \ + --labels_file $LAB/mrrate_merged_labels.csv \ + --splits_csv $LAB/splits.csv \ + --split $SPLIT --normalizer zscore \ + --out_dir ./lp_features_majority +done + +# 2) train the linear head (nn.Linear(dim, 14)) and report test AUROC +python scripts/linear_probe.py \ + --features_dir ./lp_features_majority \ + --results_dir ./lp_results_majority +``` + +Or use `submit_linear_probe.sh` in this folder (set `WEIGHTS_PATH`, +`DATA_FOLDER`, `JSONL_FILE`). + +Reproduce the labels: `python ../build_merged_group_labels.py --source majority`. diff --git a/contrastive-pretraining/scripts/eval_labels/splits_merged_majority/group_definitions.json b/contrastive-pretraining/scripts/eval_labels/splits_merged_majority/group_definitions.json new file mode 100644 index 0000000..79a3817 --- /dev/null +++ b/contrastive-pretraining/scripts/eval_labels/splits_merged_majority/group_definitions.json @@ -0,0 +1,89 @@ +{ + "PP_Cerebrovascular": [ + "Cerebral infarction", + "Cerebral hemorrhage", + "Lacunar infarct", + "Silent micro-hemorrhage of brain", + "Cavernous hemangioma", + "Subdural intracranial hemorrhage", + "Intracranial aneurysm", + "Watershed infarct" + ], + "PP_Neoplastic": [ + "Metastatic malignant neoplasm to brain", + "Intracranial meningioma", + "Glioma", + "Pituitary adenoma", + "Schwannoma" + ], + "PP_Neurodegenerative": [ + "Cerebral atrophy", + "Ventriculomegaly", + "Cerebellar degeneration" + ], + "PP_Spinal": [ + "Herniation of nucleus pulposus", + "Spinal cord compression", + "Foraminal Spinal Stenosis", + "Spinal stenosis", + "Hemangioma of vertebral column" + ], + "PP_Cystic_developmental": [ + "Arachnoid cyst", + "Cyst of pineal gland", + "Structure of cave of septum pellucidum", + "Mega cisterna magna", + "Chiari malformation", + "Rathke's pouch cyst", + "Choroid plexus cyst", + "Lipoma of brain" + ], + "PP_Infectious": [ + "Mastoiditis", + "Chronic mastoiditis" + ], + "PP_Inflammatory": [ + "Demyelinating disease of central nervous system" + ], + "PP_Unspecific_bucket": [ + "Gliosis", + "Cerebral edema", + "Encephalomalacia" + ], + "BP_Atrophies": [ + "Cerebral atrophy", + "Ventriculomegaly", + "Cerebellar degeneration" + ], + "BP_Contrast_enhancing_intracranial": [ + "Metastatic malignant neoplasm to brain", + "Intracranial meningioma", + "Glioma", + "Pituitary adenoma", + "Schwannoma" + ], + "BP_Infectious_lesions": [ + "Mastoiditis", + "Chronic mastoiditis" + ], + "BP_Edematous_lesions": [ + "Cerebral infarction", + "Lacunar infarct", + "Watershed infarct", + "Demyelinating disease of central nervous system" + ], + "BP_Hemorrhagic_lesions": [ + "Cerebral hemorrhage", + "Silent micro-hemorrhage of brain", + "Cavernous hemangioma", + "Encephalomalacia" + ], + "BP_Cystic_lesions": [ + "Arachnoid cyst", + "Cyst of pineal gland", + "Structure of cave of septum pellucidum", + "Mega cisterna magna", + "Rathke's pouch cyst", + "Choroid plexus cyst" + ] +} \ No newline at end of file diff --git a/contrastive-pretraining/scripts/eval_labels/splits_merged_majority/mrrate_merged_labels.csv b/contrastive-pretraining/scripts/eval_labels/splits_merged_majority/mrrate_merged_labels.csv new file mode 100644 index 0000000..0317460 --- /dev/null +++ b/contrastive-pretraining/scripts/eval_labels/splits_merged_majority/mrrate_merged_labels.csv @@ -0,0 +1,97897 @@ +study_uid,PP_Cerebrovascular,PP_Neoplastic,PP_Neurodegenerative,PP_Spinal,PP_Cystic_developmental,PP_Infectious,PP_Inflammatory,PP_Unspecific_bucket,BP_Atrophies,BP_Contrast_enhancing_intracranial,BP_Infectious_lesions,BP_Edematous_lesions,BP_Hemorrhagic_lesions,BP_Cystic_lesions +LEDW5KEMKI,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +4S5APY6VMJ,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +YCCKCLHIL6,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +YLLNUBJLP3,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +5CSIP5OAEB,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +327DO56MXY,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +GPOVUIHRAT,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +PEIC2HVIZN,0,1,1,0,0,0,0,1,1,1,0,0,0,0 +5FI5UDYARE,0,0,1,0,0,1,0,1,1,0,1,0,0,0 +L7OCLG3IYQ,0,0,0,0,0,0,1,0,0,0,0,1,0,0 +C5SQRPCBUA,0,0,0,0,1,0,0,0,0,0,0,0,0,1 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extract_features.py (train/val/test) -> linear_probe.py +# Class count (14) is derived from the labels CSV automatically. +#SBATCH --job-name=mrrate_lp_merged +#SBATCH --output=logs/lp_merged_%j.out +#SBATCH --error=logs/lp_merged_%j.err +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --cpus-per-task=8 +#SBATCH --gres=gpu:1 +#SBATCH --time=08:00:00 + +set -euo pipefail + +# --- paths you must set --- +WEIGHTS_PATH=${WEIGHTS_PATH:-"./mr_rate_results/MrRate.5000.pt"} +DATA_FOLDER=${DATA_FOLDER:-"/path/to/data"} +JSONL_FILE=${JSONL_FILE:-"/path/to/reports.jsonl"} + +# --- model config (must match the checkpoint used) --- +ENCODER=${ENCODER:-"vjepa2"} +FUSION_MODE=${FUSION_MODE:-"late"} +POOLING_STRATEGY=${POOLING_STRATEGY:-"simple_attn"} +NORMALIZER=${NORMALIZER:-"zscore"} +SPACE=${SPACE:-"native_space"} + +# --- locate this folder, the repo scripts, and the label files --- +LAB_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +SCRIPTS_DIR="$(cd "$LAB_DIR/../.." && pwd)" # contrastive-pretraining/scripts +FEAT_DIR=${FEAT_DIR:-"$SCRIPTS_DIR/lp_features_majority"} +RESULTS_DIR=${RESULTS_DIR:-"$SCRIPTS_DIR/lp_results_majority"} +mkdir -p "$LAB_DIR/logs" + +cd "$SCRIPTS_DIR" + +# 1) cache frozen-encoder features once per split +for SPLIT in train val test; do + echo "=== extract_features: $SPLIT ===" + python extract_features.py \ + --encoder "$ENCODER" --fusion_mode "$FUSION_MODE" \ + --pooling_strategy "$POOLING_STRATEGY" \ + --weights_path "$WEIGHTS_PATH" \ + --data_folder "$DATA_FOLDER" \ + --jsonl_file "$JSONL_FILE" \ + --labels_file "$LAB_DIR/mrrate_merged_labels.csv" \ + --splits_csv "$LAB_DIR/splits.csv" \ + --split "$SPLIT" --space "$SPACE" --normalizer "$NORMALIZER" \ + --out_dir "$FEAT_DIR" +done + +# 2) train the linear head and report test metrics +echo "=== linear_probe ===" +python linear_probe.py --features_dir "$FEAT_DIR" --results_dir "$RESULTS_DIR" +echo "Done. Results in $RESULTS_DIR" diff --git a/contrastive-pretraining/scripts/relabel_features.py b/contrastive-pretraining/scripts/relabel_features.py new file mode 100644 index 0000000..c44d923 --- /dev/null +++ b/contrastive-pretraining/scripts/relabel_features.py @@ -0,0 +1,118 @@ +"""Re-label an existing extract_features.py output for a NEW label set without +re-encoding. + +The frozen encoder never sees labels, so `features_.npy` and +`subject_ids_.txt` are identical across label schemes — only +`labels_.npy` and `label_names.json` depend on the label CSV. Running +`extract_features.py` again just to change labels would repeat the expensive 3D +encoder pass for nothing. This script reuses the cached features and rebuilds +only the label artifacts. + +It writes a new feature dir that: + - symlinks (or copies, with --copy) `features_.npy` and + `subject_ids_.txt` from the source extraction, and + - writes fresh `labels_.npy` (in the SAME subject order as the source) + and `label_names.json` from --labels_file. + +Then train as usual: python scripts/linear_probe.py --features_dir + +Example — extract features once, then probe another labels CSV with no re-encode: + + # one-time encode + for S in train val test; do + python scripts/extract_features.py ... \ + --labels_file scripts/eval_labels/splits_merged_majority/mrrate_merged_labels.csv \ + --splits_csv scripts/eval_labels/splits_merged_majority/splits.csv \ + --split $S --out_dir ./lp_features + done + + # probe any other labels CSV on the SAME cached features + python scripts/relabel_features.py \ + --features_dir ./lp_features \ + --labels_file /path/to/other_labels.csv \ + --out_dir ./lp_features_other + python scripts/linear_probe.py --features_dir ./lp_features_other --results_dir ./lp_results_other +""" +from __future__ import annotations + +import argparse +import csv +import json +import os +import shutil +from pathlib import Path + +import numpy as np + + +def load_labels(csv_path: Path): + """Return (dict study_uid -> float32 vector, list of class column names).""" + with open(csv_path) as f: + r = csv.DictReader(f) + fields = r.fieldnames or [] + id_col = "study_uid" if "study_uid" in fields else "subject_id" + cols = [c for c in fields if c != id_col] + d = {} + for row in r: + d[row[id_col]] = np.array([float(row[c]) for c in cols], dtype=np.float32) + return d, cols + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--features_dir", required=True, + help="Existing extract_features.py output (features_*.npy + subject_ids_*.txt).") + ap.add_argument("--labels_file", required=True, + help="New labels CSV: study_uid + per-class binary columns.") + ap.add_argument("--out_dir", required=True, help="New feature dir to create.") + ap.add_argument("--splits", nargs="+", default=["train", "val", "test"]) + ap.add_argument("--copy", action="store_true", + help="Copy features instead of symlinking (default: symlink).") + a = ap.parse_args() + + src = Path(a.features_dir) + out = Path(a.out_dir) + if out.resolve() == src.resolve(): + raise SystemExit("--out_dir must differ from --features_dir (would clobber features).") + out.mkdir(parents=True, exist_ok=True) + + labels, cols = load_labels(Path(a.labels_file)) + (out / "label_names.json").write_text( + json.dumps(cols, indent=2, ensure_ascii=False) + "\n") + print(f"label_names.json: {len(cols)} classes") + + n_done = 0 + for split in a.splits: + sid_path = src / f"subject_ids_{split}.txt" + feat_path = src / f"features_{split}.npy" + if not (sid_path.exists() and feat_path.exists()): + print(f"[skip] {split}: no features/subject_ids in {src}") + continue + sids = sid_path.read_text().strip().splitlines() + missing = [s for s in sids if s not in labels] + if missing: + raise SystemExit( + f"{split}: {len(missing)} subject(s) absent from {a.labels_file}; " + f"cannot relabel (would misalign features). e.g. {missing[:5]}") + Y = np.stack([labels[s] for s in sids], axis=0).astype(np.float32) + np.save(out / f"labels_{split}.npy", Y) + for name in (f"features_{split}.npy", f"subject_ids_{split}.txt"): + dst = out / name + if dst.exists() or dst.is_symlink(): + dst.unlink() + if a.copy: + shutil.copy(src / name, dst) + else: + os.symlink((src / name).resolve(), dst) + link = "copied" if a.copy else "linked" + print(f"[ok] {split}: {Y.shape[0]} subjects x {Y.shape[1]} classes (features {link})") + n_done += 1 + + if not n_done: + raise SystemExit(f"No splits found in {src}. Run extract_features.py there first.") + print(f"\nDone -> {out}\n python scripts/linear_probe.py --features_dir {out}") + + +if __name__ == "__main__": + main()