libs/adaptive/metrics.py

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)}