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6b93c3b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | #!/usr/bin/env python3
"""Train multiple models to predict k_state (parity of k) from x/y features.
Inputs: features.parquet
Output: results/ with metrics.json and model artifacts.
"""
import os, json, time, sys
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, roc_auc_score, log_loss
import xgboost as xgb
import lightgbm as lgb
OUT = "results"
os.makedirs(OUT, exist_ok=True)
def split_by_k(df, frac_train=0.70, frac_val=0.15):
"""Sequential split by k so holdout is fully unseen k-range."""
n = len(df)
i1 = int(n * frac_train); i2 = int(n * (frac_train + frac_val))
return df.iloc[:i1], df.iloc[i1:i2], df.iloc[i2:]
def main(parquet="features.parquet"):
print(f"loading {parquet}...")
df = pd.read_parquet(parquet)
print(f" rows={len(df):,} cols={len(df.columns)}")
# exclude target, traceability column k, and the giant abs_x_minus_y (string), from features.
drop = {"k", "k_state", "abs_x_minus_y"}
feat_cols = [c for c in df.columns if c not in drop]
X = df[feat_cols].astype(np.float32).values
y = df["k_state"].astype(np.int8).values
print(f" features: {len(feat_cols)} · label balance: mean={y.mean():.4f}")
df_idx = pd.DataFrame({"k": df["k"].values, "y": y})
df_idx["_i"] = np.arange(len(df_idx))
tr, va, ho = split_by_k(df_idx)
Xtr, ytr = X[tr["_i"]], y[tr["_i"]]
Xva, yva = X[va["_i"]], y[va["_i"]]
Xho, yho = X[ho["_i"]], y[ho["_i"]]
print(f" splits: train={len(ytr)} val={len(yva)} holdout={len(yho)}")
results = {}
# ---- logistic regression (sanity baseline) ----
print("\n[1] LogisticRegression ...")
t=time.time()
sc = StandardScaler().fit(Xtr)
lr = LogisticRegression(max_iter=200, n_jobs=-1).fit(sc.transform(Xtr), ytr)
pred_va = lr.predict_proba(sc.transform(Xva))[:,1]
pred_ho = lr.predict_proba(sc.transform(Xho))[:,1]
results["logreg"] = {
"val_acc": float(accuracy_score(yva, pred_va>0.5)),
"val_auc": float(roc_auc_score(yva, pred_va)),
"ho_acc": float(accuracy_score(yho, pred_ho>0.5)),
"ho_auc": float(roc_auc_score(yho, pred_ho)),
"train_s": round(time.time()-t,1),
}
print(f" val_acc={results['logreg']['val_acc']:.4f} val_auc={results['logreg']['val_auc']:.4f} "
f"ho_acc={results['logreg']['ho_acc']:.4f} ho_auc={results['logreg']['ho_auc']:.4f}")
# ---- XGBoost (CPU; flip to gpu_hist if device available) ----
print("\n[2] XGBoost ...")
t=time.time()
try:
bst = xgb.XGBClassifier(
n_estimators=400, max_depth=6, learning_rate=0.1,
tree_method="hist", device="cuda",
eval_metric="logloss", n_jobs=-1)
bst.fit(Xtr, ytr, eval_set=[(Xva, yva)], verbose=False)
except Exception as e:
print(f" GPU XGBoost failed ({e}); falling back to CPU.")
bst = xgb.XGBClassifier(
n_estimators=400, max_depth=6, learning_rate=0.1,
tree_method="hist", eval_metric="logloss", n_jobs=-1)
bst.fit(Xtr, ytr, eval_set=[(Xva, yva)], verbose=False)
pred_va = bst.predict_proba(Xva)[:,1]
pred_ho = bst.predict_proba(Xho)[:,1]
results["xgboost"] = {
"val_acc": float(accuracy_score(yva, pred_va>0.5)),
"val_auc": float(roc_auc_score(yva, pred_va)),
"ho_acc": float(accuracy_score(yho, pred_ho>0.5)),
"ho_auc": float(roc_auc_score(yho, pred_ho)),
"train_s": round(time.time()-t,1),
}
print(f" val_acc={results['xgboost']['val_acc']:.4f} val_auc={results['xgboost']['val_auc']:.4f} "
f"ho_acc={results['xgboost']['ho_acc']:.4f} ho_auc={results['xgboost']['ho_auc']:.4f}")
bst.save_model(os.path.join(OUT, "xgb.json"))
fi = dict(sorted(zip(feat_cols, bst.feature_importances_), key=lambda x: -x[1])[:10])
results["xgboost"]["top_features"] = {k:float(v) for k,v in fi.items()}
print(f" top features: {list(fi.items())[:5]}")
# ---- LightGBM ----
print("\n[3] LightGBM ...")
t=time.time()
lgbm = lgb.LGBMClassifier(n_estimators=400, max_depth=-1, num_leaves=63,
learning_rate=0.05, n_jobs=-1, verbose=-1)
lgbm.fit(Xtr, ytr, eval_set=[(Xva, yva)])
pred_va = lgbm.predict_proba(Xva)[:,1]
pred_ho = lgbm.predict_proba(Xho)[:,1]
results["lightgbm"] = {
"val_acc": float(accuracy_score(yva, pred_va>0.5)),
"val_auc": float(roc_auc_score(yva, pred_va)),
"ho_acc": float(accuracy_score(yho, pred_ho>0.5)),
"ho_auc": float(roc_auc_score(yho, pred_ho)),
"train_s": round(time.time()-t,1),
}
print(f" val_acc={results['lightgbm']['val_acc']:.4f} val_auc={results['lightgbm']['val_auc']:.4f} "
f"ho_acc={results['lightgbm']['ho_acc']:.4f} ho_auc={results['lightgbm']['ho_auc']:.4f}")
lgbm.booster_.save_model(os.path.join(OUT, "lgbm.txt"))
# ---- MLP (PyTorch, GPU if available) ----
print("\n[4] MLP (PyTorch) ...")
import torch
import torch.nn as nn
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f" device: {device}")
t=time.time()
Xs = StandardScaler().fit(Xtr)
Xtr_t = torch.tensor(Xs.transform(Xtr), dtype=torch.float32, device=device)
ytr_t = torch.tensor(ytr, dtype=torch.float32, device=device)
Xva_t = torch.tensor(Xs.transform(Xva), dtype=torch.float32, device=device)
Xho_t = torch.tensor(Xs.transform(Xho), dtype=torch.float32, device=device)
yva_t = torch.tensor(yva, dtype=torch.float32, device=device)
yho_t = torch.tensor(yho, dtype=torch.float32, device=device)
D = Xtr.shape[1]
model = nn.Sequential(
nn.Linear(D, 512), nn.ReLU(),
nn.Linear(512, 512), nn.ReLU(),
nn.Linear(512, 256), nn.ReLU(),
nn.Linear(256, 1)
).to(device)
opt = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)
crit = nn.BCEWithLogitsLoss()
BS = 8192
EPOCHS = 20
best_val_auc = 0
for ep in range(EPOCHS):
model.train()
idx = torch.randperm(len(ytr_t), device=device)
for i in range(0, len(idx), BS):
b = idx[i:i+BS]
logits = model(Xtr_t[b]).squeeze(1)
loss = crit(logits, ytr_t[b])
opt.zero_grad(); loss.backward(); opt.step()
model.eval()
with torch.no_grad():
pv = torch.sigmoid(model(Xva_t)).squeeze(1).cpu().numpy()
acc = float(accuracy_score(yva, pv>0.5))
auc = float(roc_auc_score(yva, pv))
if auc > best_val_auc:
best_val_auc = auc
torch.save(model.state_dict(), os.path.join(OUT, "mlp.pt"))
if (ep+1) % 5 == 0:
print(f" epoch {ep+1}/{EPOCHS} val_acc={acc:.4f} val_auc={auc:.4f}")
model.eval()
with torch.no_grad():
pv = torch.sigmoid(model(Xva_t)).squeeze(1).cpu().numpy()
ph = torch.sigmoid(model(Xho_t)).squeeze(1).cpu().numpy()
results["mlp"] = {
"val_acc": float(accuracy_score(yva, pv>0.5)),
"val_auc": float(roc_auc_score(yva, pv)),
"ho_acc": float(accuracy_score(yho, ph>0.5)),
"ho_auc": float(roc_auc_score(yho, ph)),
"train_s": round(time.time()-t,1),
}
print(f" val_acc={results['mlp']['val_acc']:.4f} val_auc={results['mlp']['val_auc']:.4f} "
f"ho_acc={results['mlp']['ho_acc']:.4f} ho_auc={results['mlp']['ho_auc']:.4f}")
# ---- permutation sanity check on XGBoost ----
print("\n[5] permutation sanity check (XGBoost on shuffled labels)...")
t=time.time()
yshuf = np.random.RandomState(42).permutation(ytr)
bst2 = xgb.XGBClassifier(n_estimators=200, max_depth=6, learning_rate=0.1,
tree_method="hist", n_jobs=-1)
try: bst2.set_params(device="cuda")
except: pass
bst2.fit(Xtr, yshuf)
pred = bst2.predict_proba(Xho)[:,1]
results["permutation_xgb_ho"] = {
"acc": float(accuracy_score(yho, pred>0.5)),
"auc": float(roc_auc_score(yho, pred)),
}
print(f" holdout acc on shuffled-label model = {results['permutation_xgb_ho']['acc']:.4f} "
f"auc={results['permutation_xgb_ho']['auc']:.4f} (should be ~0.5/~0.5)")
# ---- save ----
with open(os.path.join(OUT, "metrics.json"), "w") as f:
json.dump(results, f, indent=2)
print(f"\nall metrics saved to {OUT}/metrics.json")
if __name__ == "__main__":
main()
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