Datasets:
File size: 8,305 Bytes
84a242a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | #!/usr/bin/env python3
"""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")
# Prepare all substitutions before creating the output directory.
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)
# The recovered loader uses this exact ligand-cache filename.
(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()
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