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f28d994 | 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 | """Compare second-stage learners on the best high-order feature matrix."""
from __future__ import annotations
import argparse
import importlib.util
import sys
from pathlib import Path
import lightgbm as lgb
import numpy as np
import pandas as pd
import xgboost as xgb
from gensim.models import Word2Vec
from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier
from sklearn.metrics import precision_recall_curve, roc_auc_score
from sklearn.model_selection import StratifiedKFold
def load_module(name: str, path: Path):
spec = importlib.util.spec_from_file_location(name, path)
module = importlib.util.module_from_spec(spec)
assert spec.loader is not None
sys.modules[name] = module
spec.loader.exec_module(module)
return module
def best_f1(y, s):
p, r, t = precision_recall_curve(y, s)
f = 2 * p * r / (p + r + 1e-12)
i = int(np.argmax(f))
return float(f[i]), float(t[i] if i < len(t) else 0.5), float(roc_auc_score(y, s)), float(p[i]), float(r[i])
def fit_oof(X, y, kind: str, seed: int, n_splits: int):
oof = np.zeros(len(y), dtype=np.float32)
skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed)
for fold, (tr, va) in enumerate(skf.split(X, y), start=1):
if kind == "xgb_depth3":
clf = xgb.XGBClassifier(
n_estimators=900,
learning_rate=0.025,
max_depth=3,
min_child_weight=20,
subsample=0.9,
colsample_bytree=0.85,
reg_lambda=8.0,
objective="binary:logistic",
eval_metric="logloss",
tree_method="hist",
n_jobs=8,
random_state=seed + fold,
)
elif kind == "xgb_depth4":
clf = xgb.XGBClassifier(
n_estimators=800,
learning_rate=0.025,
max_depth=4,
min_child_weight=30,
subsample=0.9,
colsample_bytree=0.85,
reg_lambda=10.0,
objective="binary:logistic",
eval_metric="logloss",
tree_method="hist",
n_jobs=8,
random_state=seed + fold,
)
elif kind == "extratrees":
clf = ExtraTreesClassifier(
n_estimators=500,
max_depth=24,
min_samples_leaf=10,
max_features=0.55,
n_jobs=8,
random_state=seed + fold,
)
else:
clf = lgb.LGBMClassifier(
n_estimators=1300,
learning_rate=0.022,
num_leaves=15,
subsample=0.9,
colsample_bytree=0.9,
reg_lambda=8.0,
min_child_samples=100,
objective="binary",
n_jobs=8,
verbose=-1,
random_state=seed + fold,
)
clf.fit(X[tr], y[tr])
oof[va] = clf.predict_proba(X[va])[:, 1].astype(np.float32)
return oof
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--package-root", type=Path, default=Path(__file__).resolve().parents[1])
ap.add_argument("--split-seed", type=int, default=202)
ap.add_argument("--seed", type=int, default=202)
ap.add_argument("--n-splits", type=int, default=5)
args = ap.parse_args()
root = args.package_root.resolve()
rw = load_module("rw", root / "code/randomwalk_systematic_ablation.py")
stack = load_module("stack", root / "code/stack_rank_calibration.py")
rich = load_module("rich", root / "code/content_rich_ablation.py")
ens = load_module("ens", root / "code/generate_randomwalk_ensemble_submission.py")
high = load_module("high", root / "code/high_order_graph_stack.py")
main_val = root / "validation_runs/dynamic_seed202/dyn202_l2d512_bpr_bigbatch_more/scores/val_vanilla_ensemble_mean.npy"
train_refs, pairs, y, X_base = rw.build_base_features(root, args.split_seed, main_val)
builder = stack.ExplicitGraphFeatures(root, train_refs)
X_rich = rich.content_rich_features(root, pairs, builder)
versions = [
"dw_base_d128_l40_w10_win10",
"dw_long_d128_l80_w10_win10",
"dw_highdim_d256_l40_w10_win10",
"dw_d256_l80_w10_win10",
"dw_seed3407_d128_l40_w10_win10",
"dw_graph_ap_pp",
"n2v_p2_q1_d128_l40_w10_win10",
]
cfgs = {c.version_name: c for c in rw.small_configs() + rw.graph_configs() + rw.extra_configs()}
sys_dir = root / "validation_runs/dynamic_seed202/randomwalk_systematic"
blocks = []
for version in versions:
cfg = cfgs[version]
model = Word2Vec.load(str(sys_dir / "models" / f"{version}.model"))
block, _ = rw.pair_feature_block(model, pairs, cfg, root, args.split_seed, train_refs)
blocks.append(block)
X_high = high.build_high_order(root, train_refs, pairs, "val202")
X_high_dir = high.build_high_order_directed(root, train_refs, pairs, "val202")
X = np.column_stack([X_base, X_rich, *blocks, ens.aggregate(blocks), X_high, X_high_dir]).astype(np.float32)
print("X", X.shape)
out = root / "validation_runs/dynamic_seed202/high_order_model_compare"
out.mkdir(parents=True, exist_ok=True)
rows = []
oofs = {}
for kind in ["lgb15", "xgb_depth3", "xgb_depth4", "extratrees"]:
print("fit", kind)
oof = fit_oof(X, y, kind, args.seed + len(rows) * 41, args.n_splits)
np.save(out / f"{kind}_oof.npy", oof)
oofs[kind] = oof
f1, th, auc, p, r = best_f1(y, oof)
rows.append({"stage": kind, "validation_f1": f1, "threshold": th, "auc": auc, "precision": p, "recall": r})
print(rows[-1])
keys = list(oofs)
for a in np.linspace(0, 1, 11):
if "xgb_depth3" not in oofs:
continue
s = a * oofs["lgb15"] + (1 - a) * oofs["xgb_depth3"]
f1, th, auc, p, r = best_f1(y, s)
rows.append({"stage": f"blend_lgb_xgb3_a{a:.1f}", "validation_f1": f1, "threshold": th, "auc": auc, "precision": p, "recall": r})
if len(keys) >= 3:
s = np.mean([oofs[k] for k in keys if k != "extratrees"], axis=0)
f1, th, auc, p, r = best_f1(y, s)
rows.append({"stage": "mean_lgb_xgb3_xgb4", "validation_f1": f1, "threshold": th, "auc": auc, "precision": p, "recall": r})
pd.DataFrame(rows).sort_values("validation_f1", ascending=False).to_csv(out / "summary.csv", index=False)
print(pd.DataFrame(rows).sort_values("validation_f1", ascending=False).to_string(index=False))
if __name__ == "__main__":
main()
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