File size: 6,408 Bytes
6019d52 38bce11 6019d52 38bce11 6019d52 38bce11 6019d52 38bce11 6019d52 38bce11 6019d52 38bce11 6019d52 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | #!/usr/bin/env python3
"""Benchmark leakage-auditing character n-gram ridge baselines."""
from __future__ import annotations
import argparse
import importlib.metadata
import json
import time
from pathlib import Path
import numpy as np
from scipy.sparse import hstack
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import Ridge
from mitointeract_recovery.metrics import regression_metrics
ALPHAS = (0.1, 1.0, 10.0, 100.0)
def read_jsonl(path: Path) -> list[dict]:
with path.open() as handle:
return [json.loads(line) for line in handle if line.strip()]
def read_manifest(path: Path) -> dict[str, str]:
return {row["pair_id"]: row["split"] for row in read_jsonl(path)}
def partition(rows: list[dict], manifest: dict[str, str]) -> dict[str, list[dict]]:
result = {"train": [], "validation": [], "test": []}
for row in rows:
result[manifest[row["pair_id"]]].append(row)
return result
def targets(rows: list[dict], target_key: str) -> np.ndarray:
return np.asarray([row[target_key] for row in rows], dtype=np.float64)
def select_ridge(
train_x,
train_y: np.ndarray,
validation_x,
validation_y: np.ndarray,
) -> tuple[Ridge, float, list[dict]]:
trials = []
best = None
for alpha in ALPHAS:
model = Ridge(alpha=alpha, solver="lsqr", tol=1e-4)
model.fit(train_x, train_y)
predictions = model.predict(validation_x)
metrics = regression_metrics(validation_y, predictions)
trials.append({"alpha": alpha, "metrics": metrics})
if best is None or metrics["rmse"] < best[0]:
best = (metrics["rmse"], model, alpha)
return best[1], best[2], trials
def evaluate_feature_set(
name: str,
train_x,
validation_x,
test_x,
train_y: np.ndarray,
validation_y: np.ndarray,
test_y: np.ndarray,
) -> dict:
started = time.monotonic()
model, alpha, trials = select_ridge(train_x, train_y, validation_x, validation_y)
return {
"name": name,
"selected_alpha": alpha,
"validation_trials": trials,
"validation": regression_metrics(validation_y, model.predict(validation_x)),
"test": regression_metrics(test_y, model.predict(test_x)),
"fit_and_eval_seconds": time.monotonic() - started,
}
def benchmark_split(rows: list[dict], manifest_path: Path, target_key: str) -> dict:
manifest = read_manifest(manifest_path)
splits = partition(rows, manifest)
train_y = targets(splits["train"], target_key)
validation_y = targets(splits["validation"], target_key)
test_y = targets(splits["test"], target_key)
mean = float(train_y.mean())
result = {
"rows": {name: len(values) for name, values in splits.items()},
"mean_baseline": {
"prediction": mean,
"validation": regression_metrics(
validation_y, np.full_like(validation_y, mean)
),
"test": regression_metrics(test_y, np.full_like(test_y, mean)),
},
}
protein_vectorizer = TfidfVectorizer(
analyzer="char",
ngram_range=(3, 3),
lowercase=False,
min_df=2,
max_features=4096,
sublinear_tf=True,
dtype=np.float32,
)
ligand_vectorizer = TfidfVectorizer(
analyzer="char",
ngram_range=(2, 5),
lowercase=False,
min_df=2,
max_features=4096,
sublinear_tf=True,
dtype=np.float32,
)
protein_train = protein_vectorizer.fit_transform(
[row["sequence"] for row in splits["train"]]
)
protein_validation = protein_vectorizer.transform(
[row["sequence"] for row in splits["validation"]]
)
protein_test = protein_vectorizer.transform(
[row["sequence"] for row in splits["test"]]
)
ligand_train = ligand_vectorizer.fit_transform(
[row["smiles"] for row in splits["train"]]
)
ligand_validation = ligand_vectorizer.transform(
[row["smiles"] for row in splits["validation"]]
)
ligand_test = ligand_vectorizer.transform([row["smiles"] for row in splits["test"]])
result["feature_dimensions"] = {
"protein": protein_train.shape[1],
"ligand": ligand_train.shape[1],
}
result["models"] = [
evaluate_feature_set(
"protein_char3_ridge",
protein_train,
protein_validation,
protein_test,
train_y,
validation_y,
test_y,
),
evaluate_feature_set(
"ligand_char2_5_ridge",
ligand_train,
ligand_validation,
ligand_test,
train_y,
validation_y,
test_y,
),
evaluate_feature_set(
"combined_char_ridge",
hstack([protein_train, ligand_train], format="csr"),
hstack([protein_validation, ligand_validation], format="csr"),
hstack([protein_test, ligand_test], format="csr"),
train_y,
validation_y,
test_y,
),
]
return result
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--data-dir", type=Path, default=Path("artifacts/dev-10k"))
parser.add_argument("--target-key", default="paffinity")
parser.add_argument("--target-name", default="pAffinity")
parser.add_argument(
"--output", type=Path, default=Path("artifacts/dev-10k/baselines.json")
)
args = parser.parse_args()
rows = read_jsonl(args.data_dir / "sample.jsonl")
started = time.monotonic()
report = {
"sample_rows": len(rows),
"target": args.target_name,
"packages": {
package: importlib.metadata.version(package)
for package in ("numpy", "scipy", "scikit-learn")
},
"splits": {},
}
for manifest_path in sorted(args.data_dir.glob("split-*.jsonl")):
split_name = manifest_path.stem.removeprefix("split-")
report["splits"][split_name] = benchmark_split(
rows, manifest_path, args.target_key
)
report["total_seconds"] = time.monotonic() - started
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(report, indent=2) + "\n")
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
|