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Merge branch 'feat/moe-layer' of github.com:RYDE-WORK/lnp_ml into feat/moe-layer

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Michelle0574 2026-08-14 11:24:21 +00:00
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105
models/abl_full/rf/seed42/outer_fold_0/test_metrics.json
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99
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107
models/abl_full/rf/seed42/outer_fold_2/test_metrics.json
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103
models/abl_full/rf/seed42/outer_fold_3/test_metrics.json
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107
models/abl_full/rf/seed42/outer_fold_4/test_metrics.json
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609
models/abl_full/rf/seed42/summary.json
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"r2": 0.3898880719888205 "r2": 0.3894844727418765
}, },
"delivery": { "delivery": {
"n_samples": 60, "n_samples": 60,
"mse": 0.6215887874238653, "mse": 0.623471858388545,
"rmse": 0.7884090229213928, "rmse": 0.789602341934562,
"mae": 0.4654098044336172, "mae": 0.4688382081680016,
"r2": 0.17255759426449502 "r2": 0.17005089391015027
}, },
"toxic": { "toxic": {
"n_samples": 61, "n_samples": 61,
"accuracy": 1.0, "accuracy": 0.9344262295081968,
"precision": 1.0, "precision": 0.7142857142857143,
"recall": 1.0, "recall": 0.9655172413793103,
"f1": 1.0 "f1": 0.7821428571428571,
"true_class_counts": [
58,
3
],
"pred_class_counts": [
54,
7
],
"confusion_matrix": [
[
54,
4
],
[
0,
3
]
]
}, },
"pdi": { "pdi": {
"n_samples": 84, "n_samples": 84,
"accuracy": 0.7261904761904762, "accuracy": 0.6904761904761905,
"precision": 0.6428571428571428, "precision": 0.6693363844393593,
"recall": 0.6385630498533724, "recall": 0.717008797653959,
"f1": 0.6405581395348837 "f1": 0.662962962962963,
"true_class_counts": [
62,
22
],
"pred_class_counts": [
46,
38
],
"confusion_matrix": [
[
41,
21
],
[
5,
17
]
]
}, },
"ee": { "ee": {
"n_samples": 84, "n_samples": 84,
"accuracy": 0.6547619047619048, "accuracy": 0.6428571428571429,
"precision": 0.5676343636869953, "precision": 0.5842305037957212,
"recall": 0.5378151260504201, "recall": 0.6349206349206349,
"f1": 0.5491025070464323 "f1": 0.5925185925185925,
"true_class_counts": [
12,
21,
51
],
"pred_class_counts": [
21,
23,
40
],
"confusion_matrix": [
[
8,
2,
2
],
[
5,
12,
4
],
[
8,
9,
34
]
]
}, },
"biodist": { "biodist": {
"n_samples": 60, "n_samples": 60,
"kl_divergence": 0.11869463263891707, "kl_divergence": 0.11799692057231032,
"js_divergence": 0.02929511833384903 "js_divergence": 0.029125166530752162
} }
} }
}, },
@ -140,43 +329,106 @@
"test_metrics": { "test_metrics": {
"size": { "size": {
"n_samples": 83, "n_samples": 83,
"mse": 1.6827545971608378, "mse": 1.6732277890580922,
"rmse": 1.2972103133882484, "rmse": 1.2935330645399414,
"mae": 0.5269114175759023, "mae": 0.5211672051452704,
"r2": 0.048221123148072476 "r2": 0.05360960932568781
}, },
"delivery": { "delivery": {
"n_samples": 59, "n_samples": 59,
"mse": 0.8527625246024667, "mse": 0.8527625246024666,
"rmse": 0.9234514197306032, "rmse": 0.9234514197306032,
"mae": 0.5525073854104011, "mae": 0.5525073854104011,
"r2": 0.1710134699931437 "r2": 0.17101350859831832
}, },
"toxic": { "toxic": {
"n_samples": 60, "n_samples": 60,
"accuracy": 1.0, "accuracy": 0.9333333333333333,
"precision": 1.0, "precision": 0.7142857142857143,
"recall": 1.0, "recall": 0.9649122807017544,
"f1": 1.0 "f1": 0.7818181818181817,
"true_class_counts": [
57,
3
],
"pred_class_counts": [
53,
7
],
"confusion_matrix": [
[
53,
4
],
[
0,
3
]
]
}, },
"pdi": { "pdi": {
"n_samples": 84, "n_samples": 84,
"accuracy": 0.75, "accuracy": 0.7380952380952381,
"precision": 0.6676909569798068, "precision": 0.6612903225806452,
"recall": 0.6400293255131965, "recall": 0.6612903225806452,
"f1": 0.6493738819320214 "f1": 0.6612903225806452,
"true_class_counts": [
62,
22
],
"pred_class_counts": [
62,
22
],
"confusion_matrix": [
[
51,
11
],
[
11,
11
]
]
}, },
"ee": { "ee": {
"n_samples": 84, "n_samples": 84,
"accuracy": 0.6309523809523809, "accuracy": 0.6666666666666666,
"precision": 0.5132964178288214, "precision": 0.5872039589961117,
"recall": 0.49489693313222727, "recall": 0.6381096028154852,
"f1": 0.4975934917795383 "f1": 0.5938808373590982,
"true_class_counts": [
13,
20,
51
],
"pred_class_counts": [
23,
20,
41
],
"confusion_matrix": [
[
10,
3,
0
],
[
9,
8,
3
],
[
4,
9,
38
]
]
}, },
"biodist": { "biodist": {
"n_samples": 60, "n_samples": 60,
"kl_divergence": 0.1458100103551393, "kl_divergence": 0.14886390502458902,
"js_divergence": 0.034398655034422854 "js_divergence": 0.03503301624445018
} }
} }
}, },
@ -185,103 +437,166 @@
"test_metrics": { "test_metrics": {
"size": { "size": {
"n_samples": 84, "n_samples": 84,
"mse": 0.539048457673242, "mse": 0.5439429374081691,
"rmse": 0.7341991948192548, "rmse": 0.7375248723996833,
"mae": 0.41399459761950447, "mae": 0.41855190159962563,
"r2": 0.27868624280784415 "r2": 0.2721367833807379
}, },
"delivery": { "delivery": {
"n_samples": 58, "n_samples": 58,
"mse": 0.8685551446715326, "mse": 0.8807361070482976,
"rmse": 0.9319630597140278, "rmse": 0.9384754163260206,
"mae": 0.48785093074168856, "mae": 0.4923390269539427,
"r2": 0.16012146519578374 "r2": 0.14834272376574187
}, },
"toxic": { "toxic": {
"n_samples": 59, "n_samples": 59,
"accuracy": 1.0, "accuracy": 0.9661016949152542,
"precision": 1.0, "precision": 0.8,
"recall": 1.0, "recall": 0.9821428571428572,
"f1": 1.0 "f1": 0.865909090909091,
"true_class_counts": [
56,
3
],
"pred_class_counts": [
54,
5
],
"confusion_matrix": [
[
54,
2
],
[
0,
3
]
]
}, },
"pdi": { "pdi": {
"n_samples": 84, "n_samples": 84,
"accuracy": 0.7380952380952381, "accuracy": 0.6785714285714286,
"precision": 0.6515151515151515, "precision": 0.6428571428571428,
"recall": 0.6319648093841642, "recall": 0.6796187683284457,
"f1": 0.6390625 "f1": 0.6415362731152205,
"true_class_counts": [
62,
22
],
"pred_class_counts": [
49,
35
],
"confusion_matrix": [
[
42,
20
],
[
7,
15
]
]
}, },
"ee": { "ee": {
"n_samples": 84, "n_samples": 84,
"accuracy": 0.6785714285714286, "accuracy": 0.6309523809523809,
"precision": 0.6048840048840048, "precision": 0.5673525820584645,
"recall": 0.6175213675213675, "recall": 0.6021367521367521,
"f1": 0.6107062011796588 "f1": 0.5701075744179193,
"true_class_counts": [
12,
20,
52
],
"pred_class_counts": [
17,
28,
39
],
"confusion_matrix": [
[
7,
4,
1
],
[
6,
11,
3
],
[
4,
13,
35
]
]
}, },
"biodist": { "biodist": {
"n_samples": 58, "n_samples": 58,
"kl_divergence": 0.12253709805850461, "kl_divergence": 0.12072341813237816,
"js_divergence": 0.03003058068886421 "js_divergence": 0.029660750070876794
} }
} }
} }
], ],
"summary_stats": { "summary_stats": {
"size": { "size": {
"mse_mean": 0.840904005205596, "mse_mean": 0.8359338407622309,
"mse_std": 0.5742527703456151, "mse_std": 0.5671793909058501,
"rmse_mean": 0.8618819651219873, "rmse_mean": 0.8600962833976535,
"rmse_std": 0.3131508955808008, "rmse_std": 0.3101100192637678,
"mae_mean": 0.4343888812056213, "mae_mean": 0.43462955168345657,
"mae_std": 0.06297614136064734, "mae_std": 0.06006325295257579,
"r2_mean": 0.1895694822042235, "r2_mean": 0.19175642353368885,
"r2_std": 0.12913183030644884 "r2_std": 0.12652560874612837
}, },
"delivery": { "delivery": {
"mse_mean": 0.7062367004418524, "mse_mean": 0.705992627981044,
"mse_std": 0.17172737219125497, "mse_std": 0.1781763346919953,
"rmse_mean": 0.8332949277926908, "rmse_mean": 0.8325341145975752,
"rmse_std": 0.1088864718724354, "rmse_std": 0.11348821970704938,
"mae_mean": 0.4869573943779645, "mae_mean": 0.4872301660211962,
"mae_std": 0.048750584479951106, "mae_std": 0.048552308082167685,
"r2_mean": 0.27862494093264295, "r2_mean": 0.279413269364265,
"r2_std": 0.13730782732933916 "r2_std": 0.14494172624677884
}, },
"toxic": { "toxic": {
"accuracy_mean": 1.0, "accuracy_mean": 0.9500396342534485,
"accuracy_std": 0.0, "accuracy_std": 0.014290090778642328,
"precision_mean": 1.0, "precision_mean": 0.7457142857142858,
"precision_std": 0.0, "precision_std": 0.03149343955006944,
"recall_mean": 1.0, "recall_mean": 0.9737706334802525,
"recall_std": 0.0, "recall_std": 0.007575117962473199,
"f1_mean": 1.0, "f1_mean": 0.8148343102325404,
"f1_std": 0.0 "f1_std": 0.031251364844790935
}, },
"pdi": { "pdi": {
"accuracy_mean": 0.7571428571428571, "accuracy_mean": 0.7095238095238094,
"accuracy_std": 0.02876915707998708, "accuracy_std": 0.04677829215330594,
"precision_mean": 0.6825769014781496, "precision_mean": 0.6617886067101234,
"precision_std": 0.04965967017816626, "precision_std": 0.05613216163532689,
"recall_mean": 0.6408404643178055, "recall_mean": 0.6929975545649333,
"recall_std": 0.03805702915009694, "recall_std": 0.07132933119437145,
"f1_mean": 0.6502265285379389, "f1_mean": 0.6622063712409613,
"f1_std": 0.04335003701571735 "f1_std": 0.05828494354685521
}, },
"ee": { "ee": {
"accuracy_mean": 0.6785714285714286, "accuracy_mean": 0.6476190476190476,
"accuracy_std": 0.03984095364447979, "accuracy_std": 0.0650309537321317,
"precision_mean": 0.5966554095831669, "precision_mean": 0.5896390409265941,
"precision_std": 0.05812812328518918, "precision_std": 0.06762084933247267,
"recall_mean": 0.5809109639193673, "recall_mean": 0.62710979776526,
"recall_std": 0.055986847376187095, "recall_std": 0.08398957802940274,
"f1_mean": 0.5849460009199577, "f1_mean": 0.5915169610662443,
"f1_std": 0.056684824199185184 "f1_std": 0.07486808628755862
}, },
"biodist": { "biodist": {
"kl_divergence_mean": 0.13369116716193502, "kl_divergence_mean": 0.13306461138287579,
"kl_divergence_std": 0.01815702398766046, "kl_divergence_std": 0.017822152356421223,
"js_divergence_mean": 0.031499195810125294, "js_divergence_mean": 0.031413258472770295,
"js_divergence_std": 0.0031471347478191324 "js_divergence_std": 0.003197698848519836
} }
} }
} }

127
scripts/run_classical_baselines.py
View File

@ -13,10 +13,11 @@ import numpy as np
import pandas as pd import pandas as pd
from scipy.special import rel_entr from scipy.special import rel_entr
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.model_selection import KFold from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import StandardScaler
from sklearn.metrics import (mean_squared_error, mean_absolute_error, r2_score, from sklearn.metrics import (mean_squared_error, mean_absolute_error, r2_score,
accuracy_score, precision_score, recall_score, f1_score) accuracy_score, precision_score, recall_score, f1_score,
confusion_matrix)
from lnp_ml.dataset import LNPDataset from lnp_ml.dataset import LNPDataset
from lnp_ml.featurization.smiles import RDKitFeaturizer from lnp_ml.featurization.smiles import RDKitFeaturizer
@ -31,11 +32,31 @@ def reg_metrics(t, p):
"rmse": float(np.sqrt(mean_squared_error(t, p))), "rmse": float(np.sqrt(mean_squared_error(t, p))),
"mae": float(mean_absolute_error(t, p)), "r2": float(r2_score(t, p))} "mae": float(mean_absolute_error(t, p)), "r2": float(r2_score(t, p))}
def clf_metrics(t, p): def clf_metrics(t, p, n_classes):
return {"n_samples": int(len(p)), "accuracy": float(accuracy_score(t, p)), """Classification metrics over the declared label space.
"precision": float(precision_score(t, p, average="macro", zero_division=0)),
"recall": float(recall_score(t, p, average="macro", zero_division=0)), Supplying ``labels`` prevents a class absent from one test fold from being
"f1": float(f1_score(t, p, average="macro", zero_division=0))} silently dropped from the macro average. Counts and the confusion matrix
are retained so that apparently perfect results remain auditable.
"""
t, p = np.asarray(t, dtype=int), np.asarray(p, dtype=int)
labels = np.arange(n_classes, dtype=int)
return {
"n_samples": int(len(p)),
"accuracy": float(accuracy_score(t, p)),
"precision": float(precision_score(
t, p, labels=labels, average="macro", zero_division=0
)),
"recall": float(recall_score(
t, p, labels=labels, average="macro", zero_division=0
)),
"f1": float(f1_score(
t, p, labels=labels, average="macro", zero_division=0
)),
"true_class_counts": np.bincount(t, minlength=n_classes).astype(int).tolist(),
"pred_class_counts": np.bincount(p, minlength=n_classes).astype(int).tolist(),
"confusion_matrix": confusion_matrix(t, p, labels=labels).astype(int).tolist(),
}
def dist_metrics(t, p, eps=1e-10): def dist_metrics(t, p, eps=1e-10):
t = np.clip(np.asarray(t), eps, 1.0); p = np.clip(np.asarray(p), eps, 1.0) t = np.clip(np.asarray(t), eps, 1.0); p = np.clip(np.asarray(p), eps, 1.0)
@ -44,11 +65,13 @@ def dist_metrics(t, p, eps=1e-10):
js = float((0.5*np.sum(rel_entr(t, m), axis=-1) + 0.5*np.sum(rel_entr(p, m), axis=-1)).mean()) js = float((0.5*np.sum(rel_entr(t, m), axis=-1) + 0.5*np.sum(rel_entr(p, m), axis=-1)).mean())
return {"n_samples": int(len(p)), "kl_divergence": kl, "js_divergence": js} return {"n_samples": int(len(p)), "kl_divergence": kl, "js_divergence": js}
METRICS = {"reg": reg_metrics, "clf": clf_metrics, "dist": dist_metrics} METRICS = {"reg": reg_metrics, "dist": dist_metrics}
def score(task_type, t, p): def score(task_type, t, p, n_classes=None):
if task_type == "reg": return r2_score(t, p) if task_type == "reg": return r2_score(t, p)
if task_type == "clf": return f1_score(t, p, average="macro", zero_division=0) if task_type == "clf":
labels = np.arange(n_classes, dtype=int)
return f1_score(t, p, labels=labels, average="macro", zero_division=0)
return -dist_metrics(t, p)["js_divergence"] return -dist_metrics(t, p)["js_divergence"]
# ---------- 相似度 / 距离 ---------- # ---------- 相似度 / 距离 ----------
@ -87,9 +110,26 @@ def knn_from_matrix(S, is_sim, ytr, k, task_type, n_classes):
return np.array(out) return np.array(out)
# ---------- 各模型预测 ---------- # ---------- 各模型预测 ----------
RF_GRID = [dict(n_estimators=300, max_depth=None), # Use separate RF search spaces by task type. Classification endpoints share
dict(n_estimators=300, max_depth=12), # one small-sample regularized configuration, while regression and
dict(n_estimators=600, max_depth=None)] # biodistribution recover the original inner-CV grid. Features and outer-fold
# partitions are unchanged.
RF_REG_GRID = [
dict(n_estimators=300, max_depth=None),
dict(n_estimators=300, max_depth=12),
dict(n_estimators=600, max_depth=None),
]
RF_CLF_GRID = [
dict(
n_estimators=500,
max_depth=4,
min_samples_split=12,
min_samples_leaf=6,
max_features=0.25,
bootstrap=True,
max_samples=0.70,
)
]
K_GRID = [3, 5, 10, 15] K_GRID = [3, 5, 10, 15]
ALPHA_GRID = [0.3, 0.5, 0.7] ALPHA_GRID = [0.3, 0.5, 0.7]
@ -98,7 +138,14 @@ def rf_predict(tr, te, y, task_type, nc, params):
if task_type == "reg": if task_type == "reg":
m = RandomForestRegressor(random_state=SEED, n_jobs=-1, **params); m.fit(Xtr, ytr); return m.predict(Xte) m = RandomForestRegressor(random_state=SEED, n_jobs=-1, **params); m.fit(Xtr, ytr); return m.predict(Xte)
if task_type == "clf": if task_type == "clf":
m = RandomForestClassifier(random_state=SEED, n_jobs=-1, **params); m.fit(Xtr, ytr); return m.predict(Xte) m = RandomForestClassifier(
random_state=SEED,
n_jobs=-1,
class_weight="balanced_subsample",
**params,
)
m.fit(Xtr, ytr)
return m.predict(Xte)
m = RandomForestRegressor(random_state=SEED, n_jobs=-1, **params); m.fit(Xtr, ytr) m = RandomForestRegressor(random_state=SEED, n_jobs=-1, **params); m.fit(Xtr, ytr)
v = np.clip(m.predict(Xte), 0, None); s = v.sum(1, keepdims=True); return np.where(s > 0, v / s, v) v = np.clip(m.predict(Xte), 0, None); s = v.sum(1, keepdims=True); return np.where(s > 0, v / s, v)
@ -109,8 +156,10 @@ def knn_predict(variant, tr, te, y, task_type, nc, params):
S, is_sim = combined_dist(MORGAN[te], MORGAN[tr], TAB[te], TAB[tr], params.get("alpha", 0.5)), False S, is_sim = combined_dist(MORGAN[te], MORGAN[tr], TAB[te], TAB[tr], params.get("alpha", 0.5)), False
return knn_from_matrix(S, is_sim, y[tr], params["k"], task_type, nc) return knn_from_matrix(S, is_sim, y[tr], params["k"], task_type, nc)
def grid_for(model): def grid_for(model, task_type):
if model == "rf": return [{"rf": g} for g in RF_GRID] if model == "rf":
grid = RF_CLF_GRID if task_type == "clf" else RF_REG_GRID
return [{"rf": g} for g in grid]
if model == "tanimoto_knn": return [{"k": k} for k in K_GRID] if model == "tanimoto_knn": return [{"k": k} for k in K_GRID]
return [{"k": k, "alpha": a} for k in K_GRID for a in ALPHA_GRID] return [{"k": k, "alpha": a} for k in K_GRID for a in ALPHA_GRID]
@ -119,19 +168,41 @@ def predict(model, tr, te, y, task_type, nc, params):
else knn_predict(model, tr, te, y, task_type, nc, params) else knn_predict(model, tr, te, y, task_type, nc, params)
def select_params(model, tr, y, valid, task_type, nc, n_inner): def select_params(model, tr, y, valid, task_type, nc, n_inner):
cand = grid_for(model) cand = grid_for(model, task_type)
if len(cand) == 1: return cand[0] if len(cand) == 1: return cand[0]
n_splits = max(2, min(n_inner, len(tr) // 2)) base = tr[valid[tr]]
kf = KFold(n_splits=n_splits, shuffle=True, random_state=SEED) if len(base) < 4:
return cand[0]
if task_type == "clf":
class_counts = np.bincount(y[base].astype(int), minlength=nc)
present = class_counts[class_counts > 0]
if len(present) < 2 or int(present.min()) < 2:
return cand[0]
n_splits = min(n_inner, int(present.min()))
else:
n_splits = max(2, min(n_inner, len(base) // 2))
best, best_s = cand[0], -1e18 best, best_s = cand[0], -1e18
for p in cand: for p in cand:
scs = [] scs = []
for itr, iva in kf.split(tr): # Recreate the deterministic split iterator for every candidate.
a, b = tr[itr], tr[iva] if task_type == "clf":
a, b = a[valid[a]], b[valid[b]] split_iter = StratifiedKFold(
n_splits=n_splits, shuffle=True, random_state=SEED
).split(base, y[base])
else:
split_iter = KFold(
n_splits=n_splits, shuffle=True, random_state=SEED
).split(base)
for itr, iva in split_iter:
a, b = base[itr], base[iva]
if len(a) < 2 or len(b) < 1: continue if len(a) < 2 or len(b) < 1: continue
try: try:
scs.append(score(task_type, y[b], predict(model, a, b, y, task_type, nc, p))) scs.append(score(
task_type,
y[b],
predict(model, a, b, y, task_type, nc, p),
nc,
))
except Exception: except Exception:
pass pass
if scs and float(np.mean(scs)) > best_s: if scs and float(np.mean(scs)) > best_s:
@ -181,10 +252,14 @@ def main():
trv, tev = tr[valid[tr]], te[valid[te]] trv, tev = tr[valid[tr]], te[valid[te]]
if len(trv) < 2 or len(tev) < 1: continue if len(trv) < 2 or len(tev) < 1: continue
params = select_params(model, tr, y, valid, ttype, nc, args.n_inner) params = select_params(model, tr, y, valid, ttype, nc, args.n_inner)
m = METRICS[ttype](y[tev], predict(model, trv, tev, y, ttype, nc, params)) pred = predict(model, trv, tev, y, ttype, nc, params)
if ttype == "clf":
m = clf_metrics(y[tev], pred, nc)
else:
m = METRICS[ttype](y[tev], pred)
tm[name] = m tm[name] = m
for mk, mv in m.items(): for mk, mv in m.items():
if mk == "n_samples": continue if mk == "n_samples" or not np.isscalar(mv): continue
agg.setdefault(name, {}).setdefault(mk, []).append(mv) agg.setdefault(name, {}).setdefault(mk, []).append(mv)
fold_results.append({"fold": k, "test_metrics": tm}) fold_results.append({"fold": k, "test_metrics": tm})
fdir = Path(args.out_root) / model / f"seed{args.seed}" / f"outer_fold_{k}" fdir = Path(args.out_root) / model / f"seed{args.seed}" / f"outer_fold_{k}"
@ -203,4 +278,4 @@ def main():
print(f"[{model}] saved -> {run_dir / 'summary.json'}") print(f"[{model}] saved -> {run_dir / 'summary.json'}")
if __name__ == "__main__": if __name__ == "__main__":
main() main()