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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 | from __future__ import annotations
from typing import Dict, Iterable, Sequence
import numpy as np
from .diversity import selection_diversity
def clustering_metrics(cluster_map: Dict[str, int], hypercluster_map: Dict[int, int]) -> Dict[str, float]:
return {
"ligand_count": float(len(cluster_map)),
"cluster_count": float(len(set(cluster_map.values())) if cluster_map else 0),
"hypercluster_count": float(len(set(hypercluster_map.values())) if hypercluster_map else 0),
}
def score_metrics(scores: Sequence[float]) -> Dict[str, float]:
if not scores:
return {
"evaluated_ligand_count": 0.0,
"mean_score": 0.0,
"best_score": 0.0,
}
arr = np.asarray(scores, dtype=float)
return {
"evaluated_ligand_count": float(arr.size),
"mean_score": float(np.mean(arr)),
"best_score": float(np.min(arr)),
}
def enrichment_metrics(scores: Sequence[float], labels: Sequence[int] | None, topk: int = 10) -> Dict[str, float]:
if labels is None or not scores or len(labels) != len(scores):
return {"topk_hit_rate": 0.0, "enrichment_like": 0.0}
idx_sorted = np.argsort(np.asarray(scores, dtype=float))
labels_arr = np.asarray(labels, dtype=int)
k = min(topk, len(idx_sorted))
top_hits = int(np.sum(labels_arr[idx_sorted[:k]]))
baseline = float(np.mean(labels_arr)) if labels_arr.size else 0.0
hit_rate = top_hits / max(k, 1)
enrichment = (hit_rate / baseline) if baseline > 0 else 0.0
return {"topk_hit_rate": float(hit_rate), "enrichment_like": float(enrichment)}
def diversity_metric(selected_fingerprints: Iterable[np.ndarray]) -> Dict[str, float]:
return {"selection_diversity": selection_diversity(selected_fingerprints)}
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