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b67979a | 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 | """shared metric library. built once, used by every table.
pointwise/gene-level: mse, r2, pearson, spearman, de-gene correlation.
distributional: mmd (rbf), sliced-wasserstein.
nomination: top-k, ndcg@k (graded), true/median rank, functional (pathway) recovery,
partial overlap (combos), synergy correlation.
"""
from __future__ import annotations
import numpy as np
from scipy.stats import pearsonr, spearmanr
# ---------- pointwise / gene-level ----------
def mse(pred, true):
return float(np.mean((np.asarray(pred) - np.asarray(true)) ** 2))
def r2(pred, true):
true = np.asarray(true); pred = np.asarray(pred)
ss_res = np.sum((true - pred) ** 2)
ss_tot = np.sum((true - true.mean()) ** 2) + 1e-12
return float(1 - ss_res / ss_tot)
def pearson(pred, true):
pred, true = np.asarray(pred).ravel(), np.asarray(true).ravel()
if pred.std() < 1e-12 or true.std() < 1e-12:
return 0.0
return float(pearsonr(pred, true)[0])
def spearman(pred, true):
pred, true = np.asarray(pred).ravel(), np.asarray(true).ravel()
if pred.std() < 1e-12 or true.std() < 1e-12:
return 0.0
return float(spearmanr(pred, true)[0])
def de_gene_correlation(pred_effect, true_effect, k: int = 20):
"""pearson r over the top-k differentially-expressed genes (by |true effect|)."""
true_effect = np.asarray(true_effect).ravel()
pred_effect = np.asarray(pred_effect).ravel()
de = np.argsort(-np.abs(true_effect))[:k]
return pearson(pred_effect[de], true_effect[de])
# ---------- distributional ----------
def mmd_rbf(X, Y, gamma: float | None = None, max_n: int = 500, seed: int = 0):
rng = np.random.default_rng(seed)
X = np.asarray(X); Y = np.asarray(Y)
if len(X) > max_n:
X = X[rng.choice(len(X), max_n, replace=False)]
if len(Y) > max_n:
Y = Y[rng.choice(len(Y), max_n, replace=False)]
if gamma is None:
from scipy.spatial.distance import cdist
med = np.median(cdist(X[:200], Y[:200]) ** 2) + 1e-9
gamma = 1.0 / med
from scipy.spatial.distance import cdist
Kxx = np.exp(-gamma * cdist(X, X) ** 2)
Kyy = np.exp(-gamma * cdist(Y, Y) ** 2)
Kxy = np.exp(-gamma * cdist(X, Y) ** 2)
return float(Kxx.mean() + Kyy.mean() - 2 * Kxy.mean())
def sliced_wasserstein(X, Y, n_proj: int = 50, seed: int = 0):
"""sliced-wasserstein-1 distance (multivariate, standard approximation)."""
rng = np.random.default_rng(seed)
X = np.asarray(X); Y = np.asarray(Y)
d = X.shape[1]
dirs = rng.normal(size=(n_proj, d))
dirs /= np.linalg.norm(dirs, axis=1, keepdims=True) + 1e-12
tot = 0.0
for w in dirs:
xp = np.sort(X @ w); yp = np.sort(Y @ w)
m = min(len(xp), len(yp))
xq = np.quantile(xp, np.linspace(0, 1, m))
yq = np.quantile(yp, np.linspace(0, 1, m))
tot += np.mean(np.abs(xq - yq))
return float(tot / n_proj)
# ---------- nomination ----------
def top_k_accuracy(ranked_labels, true_label, k: int) -> float:
return float(true_label in list(ranked_labels)[:k])
def rank_of_true(ranked_labels, true_label) -> int:
ranked = list(ranked_labels)
return ranked.index(true_label) + 1 if true_label in ranked else len(ranked) + 1
def ndcg_at_k(ranked_labels, relevance: dict, k: int = 10) -> float:
"""graded ndcg@k. relevance maps label -> gain (e.g. 1 exact, 0.5 same-pathway)."""
rl = list(ranked_labels)[:k]
dcg = sum(relevance.get(l, 0.0) / np.log2(i + 2) for i, l in enumerate(rl))
ideal = sorted(relevance.values(), reverse=True)[:k]
idcg = sum(g / np.log2(i + 2) for i, g in enumerate(ideal)) + 1e-12
return float(dcg / idcg)
def exact_relevance(true_label) -> dict:
return {true_label: 1.0}
def functional_relevance(true_label, parse_fn, gene_cluster: dict, candidate_labels) -> dict:
"""graded relevance: 1.0 exact match, 0.5 if shares a functional cluster with the target."""
tgt_genes = parse_fn(true_label)
tgt_clusters = {gene_cluster.get(g) for g in tgt_genes}
rel = {}
for l in candidate_labels:
if l == true_label:
rel[l] = 1.0
else:
lg = parse_fn(l)
if any(gene_cluster.get(g) in tgt_clusters for g in lg):
rel[l] = max(rel.get(l, 0.0), 0.5)
return rel
# ---------- combinatorial ----------
def partial_overlap(pred_genes, true_genes) -> float:
a, b = set(pred_genes), set(true_genes)
return len(a & b) / max(len(b), 1)
def synergy_correlation(pred_effects, additive_effects, true_effects):
"""corr between predicted and true non-additive (synergy) components."""
pred_syn = np.asarray(pred_effects) - np.asarray(additive_effects)
true_syn = np.asarray(true_effects) - np.asarray(additive_effects)
return pearson(pred_syn.ravel(), true_syn.ravel())
# ---------- bootstrap CI / paired test ----------
def bootstrap_ci(values, n_boot: int = 2000, alpha: float = 0.05, seed: int = 0):
rng = np.random.default_rng(seed)
values = np.asarray(values, dtype=float)
means = [rng.choice(values, len(values), replace=True).mean() for _ in range(n_boot)]
lo, hi = np.quantile(means, [alpha / 2, 1 - alpha / 2])
return float(values.mean()), float(lo), float(hi)
def paired_bootstrap_pvalue(a, b, n_boot: int = 2000, seed: int = 0):
"""two-sided p-value that mean(a) != mean(b) for paired samples a,b."""
rng = np.random.default_rng(seed)
a = np.asarray(a, float); b = np.asarray(b, float)
diff = a - b
obs = diff.mean()
centered = diff - obs
count = 0
for _ in range(n_boot):
s = rng.choice(centered, len(centered), replace=True).mean()
if abs(s) >= abs(obs):
count += 1
return float((count + 1) / (n_boot + 1)), float(obs)
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