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a80f3c0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | #!/usr/bin/env python3
import argparse
import json
from collections import Counter, defaultdict
from itertools import combinations
from pathlib import Path
from typing import Dict, Iterable, List
import numpy as np
import pandas as pd
DNA = set("ACGT")
def read_jsonl(path: Path) -> List[dict]:
rows = []
with open(path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line:
rows.append(json.loads(line))
return rows
def get_sequence(row: dict) -> str:
for key in ["generated_sequence", "sequence", "reference_sequence"]:
value = row.get(key)
if isinstance(value, str) and value:
return value.upper()
return ""
def load_generated(path: Path, method: str) -> pd.DataFrame:
rows = read_jsonl(path)
out = []
for row in rows:
seq = get_sequence(row)
if not seq:
continue
out.append(
{
"method": method,
"source": row.get("source", "generated"),
"activity_bucket": row.get("activity_bucket"),
"condition_token": row.get("condition_token"),
"sequence": seq,
"prediction_sum": row.get("generated_prediction_sum", row.get("prediction_sum")),
"prediction_label_0": (
row.get("generated_prediction", [None, None])[0]
if isinstance(row.get("generated_prediction"), list)
else row.get("prediction_label_0")
),
"prediction_label_1": (
row.get("generated_prediction", [None, None])[1]
if isinstance(row.get("generated_prediction"), list) and len(row.get("generated_prediction")) > 1
else row.get("prediction_label_1")
),
"diffusion_pll": row.get("diffusion_pll"),
}
)
return pd.DataFrame(out)
def load_reference(dataset_dir: Path, split: str = "valid", limit: int = 20000) -> pd.DataFrame:
path = dataset_dir / f"{split}.parquet"
df = pd.read_parquet(path)
if limit and len(df) > limit:
df = df.sample(n=limit, random_state=42)
return pd.DataFrame(
{
"method": "reference",
"source": "reference",
"activity_bucket": None,
"condition_token": None,
"sequence": df["sequence"].astype(str).str.upper(),
"prediction_sum": np.nan,
"prediction_label_0": np.nan,
"prediction_label_1": np.nan,
"diffusion_pll": np.nan,
}
)
def gc_content(seq: str) -> float:
bases = [c for c in seq if c in DNA]
if not bases:
return np.nan
return sum(c in "GC" for c in bases) / len(bases)
def valid_dna(seq: str) -> bool:
return bool(seq) and set(seq).issubset(DNA)
def max_homopolymer(seq: str) -> int:
best = cur = 0
last = None
for char in seq:
if char == last:
cur += 1
else:
last = char
cur = 1
best = max(best, cur)
return best
def kmers(seq: str, k: int) -> Iterable[str]:
for i in range(0, max(0, len(seq) - k + 1)):
mer = seq[i : i + k]
if set(mer).issubset(DNA):
yield mer
def kmer_distribution(seqs: Iterable[str], k: int) -> Dict[str, float]:
counts = Counter()
total = 0
for seq in seqs:
for mer in kmers(seq, k):
counts[mer] += 1
total += 1
if total == 0:
return {}
return {key: value / total for key, value in counts.items()}
def js_divergence(p: Dict[str, float], q: Dict[str, float]) -> float:
keys = sorted(set(p) | set(q))
pv = np.asarray([p.get(key, 0.0) for key in keys], dtype=float)
qv = np.asarray([q.get(key, 0.0) for key in keys], dtype=float)
m = 0.5 * (pv + qv)
def kl(a, b):
mask = a > 0
return float(np.sum(a[mask] * np.log2(a[mask] / b[mask])))
return 0.5 * kl(pv, m) + 0.5 * kl(qv, m)
def hamming_distance(a: str, b: str) -> int:
n = min(len(a), len(b))
return sum(x != y for x, y in zip(a[:n], b[:n])) + abs(len(a) - len(b))
def mean_pairwise_distance(seqs: List[str], max_pairs: int = 20000) -> float:
if len(seqs) < 2:
return np.nan
pairs = list(combinations(range(len(seqs)), 2))
if len(pairs) > max_pairs:
rng = np.random.default_rng(42)
pairs = [pairs[i] for i in rng.choice(len(pairs), size=max_pairs, replace=False)]
return float(np.mean([hamming_distance(seqs[i], seqs[j]) for i, j in pairs]))
def nearest_reference_distance(seqs: List[str], refs: List[str], max_refs: int = 5000) -> List[float]:
if not refs:
return [np.nan] * len(seqs)
if len(refs) > max_refs:
rng = np.random.default_rng(42)
refs = [refs[i] for i in rng.choice(len(refs), size=max_refs, replace=False)]
out = []
for seq in seqs:
out.append(float(min(hamming_distance(seq, ref) for ref in refs)))
return out
def summarize(df: pd.DataFrame, reference_df: pd.DataFrame, k_values: List[int]) -> dict:
reference_kmers = {
k: kmer_distribution(reference_df["sequence"].tolist(), k)
for k in k_values
}
reference_sequences = reference_df["sequence"].tolist()
summary = {"methods": {}}
annotated_frames = []
for method, group in df.groupby("method"):
group = group.copy()
seqs = group["sequence"].tolist()
group["length"] = group["sequence"].str.len()
group["valid_dna"] = group["sequence"].map(valid_dna)
group["gc_content"] = group["sequence"].map(gc_content)
group["max_homopolymer"] = group["sequence"].map(max_homopolymer)
group["nearest_reference_hamming"] = nearest_reference_distance(seqs, reference_sequences)
annotated_frames.append(group)
method_summary = {
"num_sequences": int(len(group)),
"valid_dna_rate": float(group["valid_dna"].mean()),
"unique_rate": float(group["sequence"].nunique() / len(group)),
"mean_length": float(group["length"].mean()),
"mean_gc_content": float(group["gc_content"].mean()),
"mean_max_homopolymer": float(group["max_homopolymer"].mean()),
"mean_pairwise_hamming": mean_pairwise_distance(seqs),
"mean_nearest_reference_hamming": float(group["nearest_reference_hamming"].mean()),
}
for k in k_values:
method_summary[f"kmer{k}_js_to_reference"] = js_divergence(
kmer_distribution(seqs, k), reference_kmers[k]
)
for col in ["prediction_sum", "prediction_label_0", "prediction_label_1", "diffusion_pll"]:
if col in group and group[col].notna().any():
method_summary[f"mean_{col}"] = float(pd.to_numeric(group[col], errors="coerce").mean())
summary["methods"][method] = method_summary
annotated = pd.concat(annotated_frames, ignore_index=True) if annotated_frames else pd.DataFrame()
return summary, annotated
def main():
parser = argparse.ArgumentParser(description="Compute paper-level sequence quality metrics.")
parser.add_argument("--dataset_dir", required=True)
parser.add_argument("--reference_split", default="valid")
parser.add_argument("--input", action="append", default=[], help="method=path/to/jsonl. Repeatable.")
parser.add_argument("--output_dir", required=True)
parser.add_argument("--k_values", default="3,4")
args = parser.parse_args()
frames = []
for item in args.input:
method, path = item.split("=", 1)
frames.append(load_generated(Path(path), method))
reference_df = load_reference(Path(args.dataset_dir), split=args.reference_split)
frames.append(reference_df)
all_df = pd.concat(frames, ignore_index=True)
k_values = [int(k) for k in args.k_values.split(",") if k.strip()]
summary, annotated = summarize(all_df, reference_df, k_values)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
annotated.to_csv(output_dir / "sequence_metrics_rows.csv", index=False)
(output_dir / "sequence_metrics_summary.json").write_text(
json.dumps(summary, indent=2), encoding="utf-8"
)
print(output_dir / "sequence_metrics_summary.json")
if __name__ == "__main__":
main()
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