| |
| """Configure a NEW copy of recovered MolAS sources; never execute training/analysis. |
| |
| Only paths are changed. Original recovered sources remain byte-preserved. |
| Analysis requires two explicitly supplied ground-truth matrices: they are not |
| interchangeable and no matrix is generated by this utility. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import ast |
| import hashlib |
| import json |
| from pathlib import Path |
| import re |
| import shlex |
| import shutil |
| import sys |
|
|
| HERE = Path(__file__).resolve().parent |
|
|
|
|
| def replace_assignments(source: str, values: dict[str, str]) -> str: |
| """Replace named top-level assignments with explicit Python expressions.""" |
| lines = source.splitlines(keepends=True) |
| found = {} |
| for node in ast.parse(source).body: |
| if isinstance(node, ast.Assign) and len(node.targets) == 1: |
| target = node.targets[0] |
| if isinstance(target, ast.Name) and target.id in values: |
| if target.id in found: |
| raise ValueError(f"Ambiguous assignment: {target.id}") |
| found[target.id] = (node.lineno - 1, node.end_lineno) |
| if set(found) != set(values): |
| raise ValueError(f"Missing assignments: {sorted(set(values) - set(found))}") |
| for key, (start, stop) in sorted(found.items(), key=lambda pair: pair[1][0], reverse=True): |
| lines[start:stop] = [f"{key} = {values[key]}\n"] |
| return "".join(lines) |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--config", type=Path, required=True) |
| parser.add_argument("--output", type=Path, required=True, |
| help="New directory; an existing directory is never overwritten") |
| parser.add_argument("--include-analysis", action="store_true", |
| help="Also configure the primary analysis notebook; requires both explicit target paths") |
| args = parser.parse_args() |
| config_path = args.config.resolve() |
| config = json.loads(config_path.read_text()) |
| source = HERE / "historical_molas" |
| output = args.output.expanduser().resolve() |
| if output.exists() or source == output or source in output.parents: |
| raise SystemExit("Choose a new output directory outside historical_molas") |
|
|
| def path_value(key: str, kind: str) -> Path: |
| raw = config.get(key) |
| if not raw: |
| raise ValueError(f"Supply an explicit {key} in the JSON configuration") |
| path = Path(raw).expanduser() |
| if not path.is_absolute(): |
| path = config_path.parent / path |
| path = path.resolve() |
| if not (path.is_dir() if kind == "directory" else path.is_file()): |
| raise ValueError(f"{key}: {kind} does not exist: {path}") |
| return path |
|
|
| raw = path_value("raw_data_dir", "directory") |
| graphs = path_value("protein_graphs", "directory") |
| ligand = path_value("ligand_features", "file") |
| folds = path_value("fold_split", "file") |
| acquisitions = path_value("acquisition_dir", "directory") |
| if not any(acquisitions.glob("*/fold0/seed0/strict_score_matrices/strict_score_*pct.csv")): |
| raise ValueError("acquisition_dir must contain expanded policy/fold0/seed0 matrices; materialize the archives first") |
| for filename in ("moad_rmsd_trimmed.csv", "moad_pb_ratio_trimmed.csv", "moad_runtime_trimmed.csv"): |
| if not (raw / filename).is_file(): |
| raise ValueError(f"Missing raw input: {filename}") |
| analysis_paths = {} |
| if args.include_analysis: |
| analysis_paths = { |
| "downstream_dir": path_value("downstream_dir", "directory"), |
| "molas_score_matrix": path_value("molas_score_matrix", "file"), |
| "acquisition_score_matrix": path_value("acquisition_score_matrix", "file"), |
| } |
| if analysis_paths["molas_score_matrix"] == analysis_paths["acquisition_score_matrix"]: |
| raise ValueError("The two target roles must be resolved independently; do not alias them to one file") |
| if not any(analysis_paths["downstream_dir"].glob("*/seed0/b*_pct/test_composite/default/fold_*")): |
| raise ValueError("downstream_dir must contain expanded policy/seed0/bNNN_pct/test_composite/default/fold_* directories") |
|
|
| |
| data_root = output / "local_data" |
| dataset_source = (source / "data/dataset.py").read_text() |
| dataset_configured = replace_assignments(dataset_source, {"path": repr(str(data_root))}) |
| shell = (source / "main.sh").read_text() |
| shell_values = { |
| "PYTHON": str(config.get("training_python") or sys.executable), |
| "al_matrices_root": str(acquisitions), |
| "split_export_path": str(folds), |
| "log_root": str(output / "run_outputs/lightning_logs"), |
| "test_output_root": str(output / "run_outputs/test_outputs"), |
| } |
| for key, value in shell_values.items(): |
| shell, count = re.subn(rf"^{re.escape(key)}=.*$", lambda _: f"{key}={shlex.quote(value)}", |
| shell, flags=re.MULTILINE) |
| if count != 1: |
| raise ValueError(f"Expected one shell assignment for {key}, found {count}") |
|
|
| notebook = None |
| if args.include_analysis: |
| notebook = json.loads((source / "analyses/port_as_analysis.ipynb").read_text()) |
| expressions = { |
| "DATA_ROOT": f"Path({str(data_root)!r})", |
| "AL_ROOT": f"Path({str(acquisitions)!r})", |
| "GROUND_TRUTH_PATH": f"Path({str(analysis_paths['molas_score_matrix'])!r})", |
| "ACQUISITION_GROUND_TRUTH_PATH": f"Path({str(analysis_paths['acquisition_score_matrix'])!r})", |
| "RUNTIME_PATH": f"Path({str(raw / 'moad_runtime_trimmed.csv')!r})", |
| "SPLIT_PATH": f"Path({str(folds)!r})", |
| "RESULT_ROOT": f"Path({str(analysis_paths['downstream_dir'])!r})", |
| "ANALYSIS_ROOT": f"Path({str(output / 'analysis_outputs')!r})", |
| "MOLAS_REPO_ROOT": f"Path({str(output)!r})", |
| } |
| first = "".join(notebook["cells"][1]["source"]) |
| notebook["cells"][1]["source"] = replace_assignments(first, expressions).splitlines(keepends=True) |
| for cell in notebook["cells"]: |
| if cell["cell_type"] == "code": |
| content = "".join(cell["source"]).replace( |
| "/data1/home/jw1017/AS_Dock/data/MolAS/moad_runtime_trimmed.csv", |
| str(raw / "moad_runtime_trimmed.csv")) |
| cell["source"] = content.splitlines(keepends=True) |
| cell["outputs"] = [] |
| cell["execution_count"] = None |
|
|
| shutil.copytree(source, output, ignore=shutil.ignore_patterns("__pycache__", "*.pyc")) |
| data_root.mkdir() |
| for filename in ("moad_rmsd_trimmed.csv", "moad_pb_ratio_trimmed.csv", "moad_runtime_trimmed.csv"): |
| (data_root / filename).symlink_to(raw / filename) |
| (data_root / "protein_graphs_esmc_600m").symlink_to(graphs, target_is_directory=True) |
| |
| (data_root / "ligand_chemberta.pt").symlink_to(ligand) |
| (output / "data/dataset.py").write_text(dataset_configured) |
| (output / "main.sh").write_text(shell) |
| if notebook is not None: |
| (output / "analyses/port_as_analysis.ipynb").write_text(json.dumps(notebook, indent=1) + "\n") |
| manifest = { |
| "operation": "configure_only_no_training_or_analysis_execution", |
| "source_manifest": str(HERE / "historical_molas_manifest.json"), |
| "config_sha256": hashlib.sha256(config_path.read_bytes()).hexdigest(), |
| "training_paths_configured": True, |
| "primary_notebook_paths_configured": notebook is not None, |
| "runtime_probe_notebook_paths_configured": False, |
| "new_results_or_features_generated": False, |
| "scientific_settings_changed": False, |
| "warning": "main.sh retains the recovered subset, seed, and training settings; review them before any future execution. Other notebooks retain historical paths. Configuring paths does not establish historical run identity.", |
| } |
| (output / "LOCAL_CONFIGURATION.json").write_text(json.dumps(manifest, indent=2) + "\n") |
| print(json.dumps({"configured_directory": str(output), "executed_experiments": 0})) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
