LightGBM 與 CatBoost：效率與類別特徵的取捨

平滑類別統計 = (同類別的目標總和 + aP) / (同類別樣本數 + a)

r₇ᵗ = y₇ − M₆ᵗ⁻¹(x₇)

python -m pip install numpy==1.26.3 pandas==2.2.2 scikit-learn==1.5.1 lightgbm==4.4.0 catboost==1.2.5
python compare_categorical.py

import numpy as np
import pandas as pd
import sklearn
import lightgbm as lgb
import catboost
from catboost import CatBoostClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss, roc_auc_score

# Fictional independent rows; category effects and all labels are simulated.
rng = np.random.default_rng(42)
n = 1000
x0, x1 = rng.normal(size=(2, n))
group_id = rng.integers(0, 6, size=n)
group_effect = np.array([-1.0, -0.5, 0.0, 0.3, 0.7, 1.0])
score = 1.2 * x0 - 0.7 * x1 + group_effect[group_id]
probability = 1.0 / (1.0 + np.exp(-score))
y = rng.binomial(1, probability)
X = pd.DataFrame({"x0": x0, "x1": x1,
                  "group": [f"g{k}" for k in group_id]})
dev, test = train_test_split(np.arange(n), test_size=0.2,
                             stratify=y, random_state=42)
train, valid = train_test_split(dev, test_size=0.25,
                               stratify=y[dev], random_state=42)

# Fit the categorical vocabulary on train; unseen levels become missing here.
levels = sorted(X.iloc[train]["group"].unique())
X_lgb = X.copy()
X_lgb["group"] = pd.Categorical(X_lgb["group"], categories=levels)
light = lgb.LGBMClassifier(
    n_estimators=500, learning_rate=0.05, num_leaves=15,
    min_child_samples=20, reg_lambda=1.0,
    random_state=42, n_jobs=1, verbosity=-1,
    deterministic=True, force_col_wise=True,
)
light.fit(
    X_lgb.iloc[train], y[train],
    eval_set=[(X_lgb.iloc[valid], y[valid])],
    eval_metric="binary_logloss",
    callbacks=[lgb.early_stopping(30, verbose=False), lgb.log_evaluation(0)],
)

# Keep categorical strings; explicitly choose Ordered and symmetric trees.
cat = CatBoostClassifier(
    iterations=500, learning_rate=0.05, depth=4, l2_leaf_reg=3.0,
    loss_function="Logloss", eval_metric="Logloss",
    boosting_type="Ordered", grow_policy="SymmetricTree",
    random_seed=42, thread_count=1, task_type="CPU",
    allow_writing_files=False, verbose=False,
)
cat.fit(
    X.iloc[train], y[train], cat_features=["group"],
    eval_set=(X.iloc[valid], y[valid]),
    early_stopping_rounds=30, use_best_model=True,
)

print(f"versions numpy={np.__version__} pandas={pd.__version__} "
      f"sklearn={sklearn.__version__} lightgbm={lgb.__version__} "
      f"catboost={catboost.__version__}")
print(f"split train={len(train)} valid={len(valid)} test={len(test)}")
print("train_levels=" + ",".join(levels))
print(f"LightGBM selected_rounds={light.best_iteration_}")
print(f"CatBoost best_iteration_zero_based={cat.best_iteration_} "
      f"retained_trees={cat.tree_count_}")
for name, model, frame in [("LightGBM", light, X_lgb), ("CatBoost", cat, X)]:
    for split_name, indices in [("valid", valid), ("test", test)]:
        p = model.predict_proba(frame.iloc[indices])[:, 1]
        print(f"{name} {split_name} logloss={log_loss(y[indices], p):.6f} "
              f"auc={roc_auc_score(y[indices], p):.6f}")
baseline = np.full(len(test), y[train].mean())
print(f"training_prior test logloss={log_loss(y[test], baseline):.6f}")

# The fixed prior is a teaching choice, not estimated from the current label.
labels = [1, 0, 1]
prior, a = 0.5, 1.0
ordered = [(sum(labels[:i]) + a * prior) / (i + a)
           for i in range(len(labels))]
greedy = (sum(labels) + a * prior) / (len(labels) + a)
print("ordered_toy=" + ",".join(f"{z:.6f}" for z in ordered))
print(f"all_labels_smoothed={greedy:.6f}")

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