File size: 3,597 Bytes
c289d87 | 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 | from __future__ import annotations
import math
import json
import csv
from pathlib import Path
def roc_auc(scores: list[float], labels: list[int]) -> float:
pairs = [(float(s), int(y)) for s, y in zip(scores, labels)]
pos = [s for s, y in pairs if y == 1]
neg = [s for s, y in pairs if y == 0]
if not pos or not neg:
return float("nan")
wins = 0.0
for p in pos:
wins += sum(1.0 for n in neg if p < n)
wins += 0.5 * sum(1.0 for n in neg if p == n)
return float(wins / (len(pos) * len(neg)))
def enrichment_factor(scores: list[float], labels: list[int], fraction: float) -> float:
pairs = [(float(s), int(y)) for s, y in zip(scores, labels)]
n = len(pairs)
total_actives = sum(y for _, y in pairs)
if n == 0 or total_actives == 0:
return float("nan")
k = max(1, int(math.ceil(n * fraction)))
top = sorted(pairs, key=lambda item: item[0])[:k]
hit_rate_top = float(sum(y for _, y in top)) / float(k)
hit_rate_all = float(total_actives) / float(n)
return float(hit_rate_top / hit_rate_all) if hit_rate_all > 0 else float("nan")
def bedroc(scores: list[float], labels: list[int], alpha: float = 20.0) -> float:
pairs = sorted([(float(s), int(y)) for s, y in zip(scores, labels)], key=lambda item: item[0])
n = len(pairs)
n_act = sum(y for _, y in pairs)
if n == 0 or n_act == 0 or n_act == n:
return float("nan")
ranks = [idx + 1 for idx, (_, y) in enumerate(pairs) if y == 1]
denom = (1 - math.exp(-alpha)) / (math.exp(alpha / n) - 1)
rie = (n / n_act) * sum(math.exp(-alpha * rank / n) for rank in ranks) / denom
rie_min = (n / n_act) * sum(math.exp(-alpha * rank / n) for rank in range(n - n_act + 1, n + 1)) / denom
rie_max = (n / n_act) * sum(math.exp(-alpha * rank / n) for rank in range(1, n_act + 1)) / denom
return float((rie - rie_min) / (rie_max - rie_min))
def enrichment_rows(scores: list[float], labels: list[int]) -> list[dict[str, float]]:
rows = []
for frac in (0.01, 0.05, 0.10, 0.20):
rows.append({"fraction": frac, "enrichment_factor": enrichment_factor(scores, labels, frac)})
return rows
def validation_metrics_from_rows(rows: list[dict[str, object]], score_col: str = "SCORE", label_col: str = "label") -> dict[str, float]:
scores = [float(row[score_col]) for row in rows]
labels = [int(float(row.get(label_col, 0) or 0)) for row in rows]
return {
"roc_auc": roc_auc(scores, labels),
"ef1": enrichment_factor(scores, labels, 0.01),
"ef5": enrichment_factor(scores, labels, 0.05),
"ef10": enrichment_factor(scores, labels, 0.10),
"ef20": enrichment_factor(scores, labels, 0.20),
"bedroc20": bedroc(scores, labels, alpha=20.0),
}
def write_metrics(rows: list[dict[str, object]], metrics_json: str | Path, enrichment_csv: str | Path) -> dict[str, float]:
metrics = validation_metrics_from_rows(rows)
Path(metrics_json).parent.mkdir(parents=True, exist_ok=True)
Path(metrics_json).write_text(json.dumps(metrics, indent=2), encoding="utf-8")
scores = [float(row["SCORE"]) for row in rows]
labels = [int(float(row.get("label", 0) or 0)) for row in rows]
with Path(enrichment_csv).open("w", encoding="utf-8", newline="") as handle:
writer = csv.DictWriter(handle, fieldnames=["fraction", "enrichment_factor"])
writer.writeheader()
writer.writerows(enrichment_rows(scores, labels))
return metrics
validation_metrics_from_table = validation_metrics_from_rows
enrichment_table = enrichment_rows
|