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from __future__ import annotations
import hashlib
import importlib.metadata
import json
import platform
from pathlib import Path
import sys
from typing import Any, Dict, Mapping, MutableMapping, Sequence
import numpy as np
import pandas as pd
from rdkit import Chem
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from src.data import MolecularFeatureExtractor
from revision.scripts.reanalysis_core import (
annotate_structures,
compute_regression_metrics,
ensure_new_output_dir,
make_grouped_folds,
make_grouped_holdout,
paired_group_bootstrap,
per_lab_metrics,
)
PRIMARY_CLASSICAL_MODEL = "fingerprint_plus_descriptors_et"
ALLOWED_GROUP_COLUMNS = {
"structure_group",
"scaffold_group",
"scaffold_component_group",
}
def _json_default(value: Any) -> Any:
if isinstance(value, (np.integer,)):
return int(value)
if isinstance(value, (np.floating,)):
return float(value)
if isinstance(value, np.ndarray):
return value.tolist()
if isinstance(value, Path):
return str(value)
raise TypeError(f"Cannot serialize {type(value).__name__}")
def _write_json(path: Path, payload: Mapping[str, Any]) -> None:
path.write_text(
json.dumps(payload, indent=2, ensure_ascii=False, default=_json_default) + "\n",
encoding="utf-8",
)
def _resolve_path(path_value: str | Path, project_root: Path) -> Path:
path = Path(path_value)
return (project_root / path).resolve() if not path.is_absolute() else path.resolve()
def _portable_path(path_value: str | Path, project_root: Path) -> str:
"""Represent a path without recording a machine-specific absolute path."""
path = Path(path_value).resolve()
root = project_root.resolve()
try:
return path.relative_to(root).as_posix()
except ValueError:
# External inputs/outputs (for example, a temporary smoke-test directory)
# are deliberately identified without leaking the host directory layout.
return (Path("external") / path.name).as_posix()
def _portable_config(config: Mapping[str, Any], project_root: Path) -> Dict[str, Any]:
portable = dict(config)
for key in ("input_csv", "output_root"):
if key in portable:
portable[key] = _portable_path(portable[key], project_root)
return portable
def _package_version(distribution_name: str) -> str:
try:
return importlib.metadata.version(distribution_name)
except importlib.metadata.PackageNotFoundError:
return "not installed"
def _environment_record() -> Dict[str, Any]:
"""Return reproducibility-critical versions without host-specific paths."""
return {
"python": {
"version": platform.python_version(),
"implementation": platform.python_implementation(),
},
"platform": {
"system": platform.system(),
"release": platform.release(),
"machine": platform.machine(),
},
"packages": {
name: _package_version(name)
for name in (
"joblib",
"numpy",
"pandas",
"rdkit",
"scikit-learn",
"scipy",
"torch",
"torch-geometric",
)
},
"python_major_minor": f"{sys.version_info.major}.{sys.version_info.minor}",
}
def _write_sha256_manifest(output_root: Path) -> None:
"""Hash every completed artifact using output-root-relative paths."""
manifest = output_root / "SHA256SUMS.txt"
lines = []
for artifact in sorted(path for path in output_root.rglob("*") if path.is_file() and path != manifest):
digest = hashlib.sha256()
with artifact.open("rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(chunk)
lines.append(f"{digest.hexdigest()} {artifact.relative_to(output_root).as_posix()}")
manifest.write_text("\n".join(lines) + "\n", encoding="utf-8")
def validate_config(config: Mapping[str, Any], project_root: Path) -> Dict[str, Any]:
"""Validate predeclared design choices before any output is created."""
required = ("input_csv", "output_root", "outer_seeds", "split_strategies")
missing = [key for key in required if key not in config]
if missing:
raise ValueError(f"Configuration is missing required keys: {missing}")
normalized = dict(config)
input_csv = _resolve_path(config["input_csv"], project_root)
output_root = _resolve_path(config["output_root"], project_root)
original_model_dir = (project_root / "hybrid_oof_models").resolve()
if output_root == original_model_dir or original_model_dir in output_root.parents:
raise ValueError("output_root must not be hybrid_oof_models or one of its descendants.")
if not input_csv.is_file():
raise FileNotFoundError(f"Input CSV not found: {input_csv}")
seeds = [int(seed) for seed in config["outer_seeds"]]
if not seeds or len(seeds) != len(set(seeds)):
raise ValueError("outer_seeds must be a nonempty list of unique predeclared seeds.")
raw_strategies = dict(config["split_strategies"])
if not raw_strategies:
raise ValueError("At least one split strategy is required.")
strategy_specs: Dict[str, Dict[str, Any]] = {}
for strategy_name, raw_spec in raw_strategies.items():
if isinstance(raw_spec, str):
spec = {
"group_column": raw_spec,
"outer_folds": int(config.get("outer_folds", 10)),
"balance_group_sizes": False,
}
elif isinstance(raw_spec, Mapping):
spec = {
"group_column": str(raw_spec["group_column"]),
"outer_folds": int(raw_spec.get("outer_folds", config.get("outer_folds", 10))),
"balance_group_sizes": bool(raw_spec.get("balance_group_sizes", False)),
}
else:
raise TypeError(f"Invalid split strategy specification for {strategy_name!r}.")
if spec["outer_folds"] < 2:
raise ValueError(f"outer_folds must be at least 2 for {strategy_name!r}.")
strategy_specs[str(strategy_name)] = spec
unknown_groups = {
spec["group_column"] for spec in strategy_specs.values()
} - ALLOWED_GROUP_COLUMNS
if unknown_groups:
raise ValueError(f"Unsupported split group columns: {sorted(unknown_groups)}")
normalized["input_csv"] = str(input_csv)
normalized["output_root"] = str(output_root)
normalized["outer_seeds"] = seeds
normalized["split_strategies"] = strategy_specs
normalized.setdefault("outer_folds", 10)
normalized.setdefault("inner_folds", 6)
normalized.setdefault("analysis_id", output_root.name)
normalized.setdefault("descriptor_features", ["LogP", "BertzCT", "MolMR", "NumAromaticRings", "HeavyAtomCount"])
normalized.setdefault("fingerprint", {"radius": 2, "n_bits": 2048, "use_chirality": True})
normalized.setdefault(
"classical_baselines",
{"n_estimators": 500, "max_depth": None, "min_samples_leaf": 2, "max_features": 1.0},
)
normalized.setdefault("bootstrap", {"n_resamples": 2000, "confidence": 0.95})
return normalized
def _fit_lab_encoder(train_labels: np.ndarray, test_labels: np.ndarray) -> tuple[np.ndarray, np.ndarray, OneHotEncoder]:
encoder = OneHotEncoder(handle_unknown="ignore", sparse_output=False, dtype=np.float32)
train_one_hot = encoder.fit_transform(train_labels.reshape(-1, 1))
test_one_hot = encoder.transform(test_labels.reshape(-1, 1))
return train_one_hot, test_one_hot, encoder
def run_classical_baselines(
*,
fingerprints: np.ndarray,
descriptors: np.ndarray,
lab_labels: Sequence[object],
targets: Sequence[float],
train_indices: Sequence[int],
test_indices: Sequence[int],
seed: int,
estimator_config: Mapping[str, Any],
) -> Dict[str, Any]:
"""Fit fixed, untuned classical baselines and one descriptor ablation.
The primary model is predeclared as Morgan fingerprint + fixed descriptors
+ one-hot laboratory ExtraTrees. Test performance is never used to select
among these models.
"""
fingerprints_array = np.asarray(fingerprints, dtype=np.float32)
descriptors_array = np.asarray(descriptors, dtype=np.float32)
labs = np.asarray(lab_labels).astype(str)
y = np.asarray(targets, dtype=float)
train = np.asarray(train_indices, dtype=int)
test = np.asarray(test_indices, dtype=int)
lab_train, lab_test, lab_encoder = _fit_lab_encoder(labs[train], labs[test])
descriptor_train = np.column_stack([descriptors_array[train], lab_train])
descriptor_test = np.column_stack([descriptors_array[test], lab_test])
fingerprint_train = np.column_stack([fingerprints_array[train], lab_train])
fingerprint_test = np.column_stack([fingerprints_array[test], lab_test])
combined_train = np.column_stack([fingerprints_array[train], descriptors_array[train], lab_train])
combined_test = np.column_stack([fingerprints_array[test], descriptors_array[test], lab_test])
tree_parameters = {
"n_estimators": int(estimator_config.get("n_estimators", 500)),
"max_depth": estimator_config.get("max_depth"),
"min_samples_leaf": int(estimator_config.get("min_samples_leaf", 2)),
"max_features": estimator_config.get("max_features", 1.0),
"random_state": int(seed),
"n_jobs": int(estimator_config.get("n_jobs", -1)),
}
models: MutableMapping[str, Any] = {
"descriptor_only_ridge": make_pipeline(StandardScaler(), Ridge(alpha=1.0)),
"descriptor_only_et": ExtraTreesRegressor(**tree_parameters),
"fingerprint_no_lab_et": ExtraTreesRegressor(**tree_parameters),
"fingerprint_only_et": ExtraTreesRegressor(**tree_parameters),
"fingerprint_plus_descriptors_et": ExtraTreesRegressor(**tree_parameters),
}
feature_pairs = {
"descriptor_only_ridge": (descriptor_train, descriptor_test),
"descriptor_only_et": (descriptor_train, descriptor_test),
"fingerprint_no_lab_et": (fingerprints_array[train], fingerprints_array[test]),
"fingerprint_only_et": (fingerprint_train, fingerprint_test),
"fingerprint_plus_descriptors_et": (combined_train, combined_test),
}
predictions: Dict[str, np.ndarray] = {}
for name, model in models.items():
train_features, test_features = feature_pairs[name]
model.fit(train_features, y[train])
predictions[name] = np.asarray(model.predict(test_features), dtype=float)
no_lab_model = models["fingerprint_no_lab_et"]
no_lab_train_predictions = np.asarray(
no_lab_model.predict(fingerprints_array[train]),
dtype=float,
)
affine_by_lab: Dict[str, tuple[float, float]] = {}
global_offset = float(np.mean(y[train] - no_lab_train_predictions))
for lab in sorted(np.unique(labs[train])):
lab_mask = labs[train] == lab
lab_y = y[train][lab_mask]
lab_base = no_lab_train_predictions[lab_mask]
if lab_mask.sum() >= 3 and np.ptp(lab_base) > 1e-8:
slope, intercept = np.polyfit(lab_base, lab_y, 1)
affine_by_lab[lab] = (float(slope), float(intercept))
else:
affine_by_lab[lab] = (1.0, float(np.mean(lab_y - lab_base)))
predictions["fingerprint_no_lab_plus_lab_affine"] = np.asarray(
[
affine_by_lab.get(lab, (1.0, global_offset))[0] * base_prediction
+ affine_by_lab.get(lab, (1.0, global_offset))[1]
for lab, base_prediction in zip(
labs[test],
predictions["fingerprint_no_lab_et"],
)
],
dtype=float,
)
global_median = float(np.median(y[train]))
laboratory_medians = pd.Series(y[train]).groupby(labs[train]).median().to_dict()
predictions["lab_median"] = np.asarray(
[laboratory_medians.get(lab, global_median) for lab in labs[test]],
dtype=float,
)
metrics = {name: compute_regression_metrics(y[test], values) for name, values in predictions.items()}
return {
"primary_model": PRIMARY_CLASSICAL_MODEL,
"predictions": predictions,
"metrics": metrics,
"models": dict(models),
"lab_encoder": lab_encoder,
"lab_affine_parameters": affine_by_lab,
"feature_dimensions": {
"descriptor_only": int(descriptor_train.shape[1]),
"fingerprint_only": int(fingerprint_train.shape[1]),
"fingerprint_plus_descriptors": int(combined_train.shape[1]),
},
}
def _extract_features(
annotated: pd.DataFrame,
descriptor_features: Sequence[str],
fingerprint_config: Mapping[str, Any],
) -> tuple[np.ndarray, np.ndarray]:
extractor = MolecularFeatureExtractor()
descriptor_rows = []
fingerprints = []
for smiles in annotated["SMILES"].astype(str):
descriptor_record = extractor.get_molecular_descriptors(smiles)
missing = [name for name in descriptor_features if name not in descriptor_record]
if missing:
raise ValueError(f"Descriptor extraction did not produce: {missing}")
descriptor_rows.append([float(descriptor_record[name]) for name in descriptor_features])
fingerprints.append(
extractor.get_morgan_fingerprint(
smiles,
n_bits=int(fingerprint_config.get("n_bits", 2048)),
radius=int(fingerprint_config.get("radius", 2)),
use_chirality=bool(fingerprint_config.get("use_chirality", True)),
)
)
return np.asarray(descriptor_rows, dtype=np.float32), np.asarray(fingerprints, dtype=np.float32)
def _dataset_audit(annotated: pd.DataFrame) -> Dict[str, Any]:
labs_per_structure = annotated.groupby("structure_group")["Lab"].nunique()
canonical_lab_rt_counts = annotated.groupby(["structure_group", "Lab"])["RT"].nunique()
return {
"n_rows": int(len(annotated)),
"columns": [str(column) for column in annotated.columns],
"n_laboratories": int(annotated["Lab"].nunique()),
"laboratory_counts": annotated["Lab"].value_counts().sort_index().astype(int).to_dict(),
"unique_smiles_as_supplied": int(annotated["SMILES"].nunique()),
"unique_canonical_isomeric_smiles": int(annotated["canonical_smiles_isomeric"].nunique()),
"unique_canonical_nonisomeric_smiles": int(annotated["canonical_smiles_nonisomeric"].nunique()),
"unique_full_inchi_keys": int(annotated["inchi_key_full"].nunique()),
"unique_connectivity_blocks": int(annotated["inchi_key_connectivity"].nunique()),
"unique_raw_murcko_scaffold_groups": int(annotated["scaffold_group"].nunique()),
"unique_identity_safe_scaffold_components": int(
annotated["scaffold_component_group"].nunique()
),
"graph_conversion_failures_in_provided_csv": 0,
"upstream_curation_removals": "not inferable without the pre-curation export",
"identity_policy": {
"outer_group_key": "InChIKey connectivity block",
"fallback": "canonical non-isomeric SMILES when InChI generation fails",
"salt_handling": "no fragment removal or parent selection; fragment_count is recorded",
"stereochemistry_handling": "full identity is recorded; connectivity grouping conservatively co-groups stereoisomers",
"tautomer_handling": "no explicit tautomer canonicalizer; InChI connectivity grouping supplies the conservative boundary",
"protonation_handling": "no neutralization; supplied formal charge and full structure remain recorded",
},
"fragment_count_distribution": annotated["fragment_count"].value_counts().sort_index().astype(int).to_dict(),
"formal_charge_distribution": annotated["formal_charge"].value_counts().sort_index().astype(int).to_dict(),
"rows_with_explicit_stereo": int(annotated["has_explicit_stereo"].sum()),
"labs_per_structure_distribution": labs_per_structure.value_counts().sort_index().astype(int).to_dict(),
"structures_seen_in_multiple_labs": int((labs_per_structure > 1).sum()),
"rows_in_multi_lab_structures": int(annotated["structure_group"].isin(labs_per_structure[labs_per_structure > 1].index).sum()),
"exact_duplicate_extra_rows": int(annotated.duplicated(["SMILES", "Lab", "RT"]).sum()),
"structure_lab_duplicate_extra_rows": int(annotated.duplicated(["structure_group", "Lab"]).sum()),
"structure_lab_groups_with_conflicting_rt": int((canonical_lab_rt_counts > 1).sum()),
}
def _write_shared_lab_matrix(annotated: pd.DataFrame, output_path: Path) -> None:
labs = sorted(annotated["Lab"].astype(str).unique())
matrix = pd.DataFrame(0, index=labs, columns=labs, dtype=int)
for _, group in annotated.groupby("structure_group"):
represented = sorted(group["Lab"].astype(str).unique())
for left in represented:
for right in represented:
matrix.loc[left, right] += 1
matrix.index.name = "Lab"
matrix.to_csv(output_path)
def _split_summary(
annotated: pd.DataFrame,
train_indices: np.ndarray,
test_indices: np.ndarray,
group_column: str,
) -> Dict[str, Any]:
train_groups = set(annotated.loc[train_indices, group_column])
test_groups = set(annotated.loc[test_indices, group_column])
train_structures = set(annotated.loc[train_indices, "structure_group"])
test_structures = set(annotated.loc[test_indices, "structure_group"])
return {
"n_development_rows": int(len(train_indices)),
"n_test_rows": int(len(test_indices)),
"test_fraction": float(len(test_indices) / len(annotated)),
"group_column": group_column,
"hard_group_overlap": int(len(train_groups & test_groups)),
"structure_identity_overlap": int(len(train_structures & test_structures)),
"development_laboratories": int(annotated.loc[train_indices, "Lab"].nunique()),
"test_laboratories": int(annotated.loc[test_indices, "Lab"].nunique()),
}
def _write_split_outputs(
*,
split_dir: Path,
annotated: pd.DataFrame,
train_indices: np.ndarray,
test_indices: np.ndarray,
inner_folds: Sequence[tuple[np.ndarray, np.ndarray]],
strategy: str,
group_column: str,
seed: int,
) -> None:
assignments = annotated.copy()
assignments["outer_split"] = "development"
assignments.loc[test_indices, "outer_split"] = "test"
assignments["inner_validation_fold"] = -1
for fold_index, (_, validation_indices) in enumerate(inner_folds):
assignments.loc[validation_indices, "inner_validation_fold"] = fold_index
assignments["split_strategy"] = strategy
assignments["outer_seed"] = int(seed)
assignments.to_csv(split_dir / "split_assignments.csv", index=False)
np.savez_compressed(
split_dir / "split_indices.npz",
development_indices=np.asarray(train_indices, dtype=np.int32),
test_indices=np.asarray(test_indices, dtype=np.int32),
inner_train_indices=np.asarray([pair[0] for pair in inner_folds], dtype=object),
inner_validation_indices=np.asarray([pair[1] for pair in inner_folds], dtype=object),
)
_write_json(
split_dir / "split_summary.json",
_split_summary(annotated, train_indices, test_indices, group_column),
)
def _write_classical_outputs(
*,
split_dir: Path,
annotated: pd.DataFrame,
test_indices: np.ndarray,
result: Mapping[str, Any],
bootstrap_config: Mapping[str, Any],
seed: int,
) -> None:
y_true = annotated.loc[test_indices, "RT"].to_numpy(dtype=float)
labs = annotated.loc[test_indices, "Lab"].astype(str).to_numpy()
groups = annotated.loc[test_indices, "structure_group"].astype(str).to_numpy()
predictions = result["predictions"]
prediction_frame = annotated.loc[
test_indices,
[
"record_index",
"SMILES",
"Lab",
"RT",
"structure_group",
"scaffold_group",
"scaffold_component_group",
],
].copy()
for model_name, values in predictions.items():
prediction_frame[f"prediction_{model_name}"] = values
prediction_frame.to_csv(split_dir / "classical_predictions.csv", index=False)
metrics_payload = {
"primary_model_predeclared": result["primary_model"],
"metrics": result["metrics"],
"feature_dimensions": result["feature_dimensions"],
"selection_rule": "No outer-test model selection; all fixed models are reported.",
}
_write_json(split_dir / "classical_metrics.json", metrics_payload)
development_ranges = (
annotated.loc[annotated.index.difference(test_indices)]
.groupby("Lab")["RT"]
.agg(lambda values: float(values.max() - values.min()))
.to_dict()
)
per_lab_tables = []
for model_name, values in predictions.items():
table = per_lab_metrics(
y_true,
values,
labs,
normalization_ranges=development_ranges,
)
table.insert(0, "model", model_name)
per_lab_tables.append(table)
pd.concat(per_lab_tables, ignore_index=True).to_csv(split_dir / "classical_per_lab_metrics.csv", index=False)
primary_predictions = predictions[result["primary_model"]]
paired = {}
for reference_name, reference_predictions in predictions.items():
if reference_name == result["primary_model"]:
continue
paired[reference_name] = paired_group_bootstrap(
y_true=y_true,
candidate=primary_predictions,
reference=reference_predictions,
groups=groups,
n_resamples=int(bootstrap_config.get("n_resamples", 2000)),
confidence=float(bootstrap_config.get("confidence", 0.95)),
seed=int(seed),
)
_write_json(split_dir / "classical_paired_group_bootstrap.json", paired)
def run_reanalysis(
config: Mapping[str, Any],
*,
project_root: Path,
include_neural: bool = False,
smoke_only: bool = False,
) -> Path:
"""Run audit, fixed splits, classical baselines, and optional neural stack."""
project_root = project_root.resolve()
normalized = validate_config(config, project_root)
output_root = ensure_new_output_dir(normalized["output_root"])
data = pd.read_csv(normalized["input_csv"])
if smoke_only and len(data) > 240:
data = data.groupby("Lab", group_keys=False).head(8).reset_index(drop=True)
annotated = annotate_structures(data)
input_hash = hashlib.sha256(Path(normalized["input_csv"]).read_bytes()).hexdigest()
run_metadata = {
"analysis_id": normalized["analysis_id"],
"input_csv": _portable_path(normalized["input_csv"], project_root),
"input_sha256": input_hash,
"output_root": _portable_path(output_root, project_root),
"outer_seeds_predeclared": normalized["outer_seeds"] if not smoke_only else normalized["outer_seeds"][:1],
"outer_fold_selection": (
"Canonical grouping uses the first shuffled StratifiedGroupKFold fold. "
"Size-balanced strategies choose the fold with maximal laboratory coverage "
"and closest row count to 1/k; RT values and model results are never used."
),
"smoke_only": bool(smoke_only),
"include_neural": bool(include_neural),
"original_outputs_modified": False,
}
_write_json(output_root / "RUN_METADATA.json", run_metadata)
_write_json(output_root / "FROZEN_CONFIG.json", _portable_config(normalized, project_root))
_write_json(output_root / "ENVIRONMENT.json", _environment_record())
_write_json(output_root / "dataset_audit.json", _dataset_audit(annotated))
_write_shared_lab_matrix(annotated, output_root / "shared_compounds_by_lab.csv")
annotated.to_csv(output_root / "record_identity_manifest.csv", index=False)
descriptor_matrix, fingerprint_matrix = _extract_features(
annotated,
normalized["descriptor_features"],
normalized["fingerprint"],
)
seeds = normalized["outer_seeds"][:1] if smoke_only else normalized["outer_seeds"]
inner_fold_count = 2 if smoke_only else int(normalized["inner_folds"])
estimator_config = dict(normalized["classical_baselines"])
if smoke_only:
estimator_config["n_estimators"] = min(8, int(estimator_config.get("n_estimators", 8)))
bootstrap_config = dict(normalized["bootstrap"])
if smoke_only:
bootstrap_config["n_resamples"] = min(50, int(bootstrap_config.get("n_resamples", 50)))
for strategy, strategy_spec in normalized["split_strategies"].items():
group_column = strategy_spec["group_column"]
outer_folds = 2 if smoke_only else int(strategy_spec["outer_folds"])
balance_group_sizes = bool(strategy_spec.get("balance_group_sizes", False))
for seed in seeds:
split_dir = output_root / strategy / f"seed_{seed}"
split_dir.mkdir(parents=True, exist_ok=False)
train_indices, test_indices = make_grouped_holdout(
annotated,
group_column=group_column,
seed=seed,
n_splits=outer_folds,
balance_group_sizes=balance_group_sizes,
)
inner_folds = make_grouped_folds(
annotated,
train_indices,
group_column=group_column,
seed=seed,
n_splits=inner_fold_count,
)
_write_split_outputs(
split_dir=split_dir,
annotated=annotated,
train_indices=train_indices,
test_indices=test_indices,
inner_folds=inner_folds,
strategy=strategy,
group_column=group_column,
seed=seed,
)
classical_result = run_classical_baselines(
fingerprints=fingerprint_matrix,
descriptors=descriptor_matrix,
lab_labels=annotated["Lab"].to_numpy(),
targets=annotated["RT"].to_numpy(),
train_indices=train_indices,
test_indices=test_indices,
seed=seed,
estimator_config=estimator_config,
)
_write_classical_outputs(
split_dir=split_dir,
annotated=annotated,
test_indices=test_indices,
result=classical_result,
bootstrap_config=bootstrap_config,
seed=seed,
)
if include_neural:
from revision.scripts.reanalysis_neural import run_neural_stack
run_neural_stack(
normalized,
annotated=annotated,
descriptor_matrix=descriptor_matrix,
fingerprint_matrix=fingerprint_matrix,
train_indices=train_indices,
test_indices=test_indices,
inner_folds=inner_folds,
output_dir=split_dir / "neural_stack",
seed=seed,
smoke_only=smoke_only,
)
_write_sha256_manifest(output_root)
return output_root
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