| name | scientific-ensemble-methods |
| description | アンサンブル学習スキル。Stacking/Blending 多段積層・
Boosting (XGBoost/LightGBM/CatBoost) 勾配ブースティング・
Bagging/Random Subspace・Voting 分類器/回帰器・
アンサンブル多様性評価・モデル統合パイプライン。
|
| tu_tools | [{"key":"openml","name":"OpenML","description":"アンサンブル手法ベンチマーク参照"}] |
Scientific Ensemble Methods
複数モデルの組み合わせによる予測精度向上・安定化を実現する
アンサンブル学習手法の設計・評価パイプラインを提供する。
When to Use
- XGBoost/LightGBM/CatBoost で勾配ブースティングを実行するとき
- Stacking/Blending で多段アンサンブルを構築するとき
- 複数モデルの Voting/Averaging で安定予測を得るとき
- アンサンブルの多様性を評価するとき
- Out-of-Fold 予測でリーク防止を行うとき
- モデルの寄与度を分析するとき
Quick Start
1. 勾配ブースティング比較
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score
def compare_boosting(X, y, cv=5, scoring="f1_macro",
task="classification"):
"""
XGBoost / LightGBM / CatBoost 比較。
Parameters:
X: np.ndarray — 特徴量
y: np.ndarray — ラベル
cv: int — CV 分割数
scoring: str — 評価指標
task: str — "classification" / "regression"
"""
results = []
try:
from xgboost import XGBClassifier, XGBRegressor
model = (XGBClassifier(n_estimators=200, max_depth=6,
learning_rate=0.1, random_state=42,
use_label_encoder=False, eval_metric="logloss")
if task == "classification"
else XGBRegressor(n_estimators=200, max_depth=6,
learning_rate=0.1, random_state=42))
scores = cross_val_score(model, X, y, cv=cv, scoring=scoring)
results.append({"model": "XGBoost", "mean": scores.mean(),
"std": scores.std()})
except ImportError:
pass
try:
from lightgbm import LGBMClassifier, LGBMRegressor
model = (LGBMClassifier(n_estimators=200, max_depth=6,
learning_rate=0.1, random_state=42, verbose=-1)
if task == "classification"
else LGBMRegressor(n_estimators=200, max_depth=6,
learning_rate=0.1, random_state=42, verbose=-1))
scores = cross_val_score(model, X, y, cv=cv, scoring=scoring)
results.append({"model": "LightGBM", "mean": scores.mean(),
"std": scores.std()})
except ImportError:
pass
try:
from catboost import CatBoostClassifier, CatBoostRegressor
model = (CatBoostClassifier(iterations=200, depth=6,
learning_rate=0.1, random_seed=42, verbose=0)
if task == "classification"
else CatBoostRegressor(iterations=200, depth=6,
learning_rate=0.1, random_seed=42, verbose=0))
scores = cross_val_score(model, X, y, cv=cv, scoring=scoring)
results.append({"model": "CatBoost", "mean": scores.mean(),
"std": scores.std()})
except ImportError:
pass
df = pd.DataFrame(results).sort_values("mean", ascending=False)
if not df.empty:
print(f"Boosting: best = {df.iloc[0]['model']} "
f"({scoring} = {df.iloc[0]['mean']:.4f})")
return df
2. Stacking アンサンブル
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
def stacking_ensemble(X_train, y_train, X_test,
base_models=None, meta_model=None,
n_folds=5):
"""
Stacking アンサンブル (Out-of-Fold 予測)。
Parameters:
X_train: np.ndarray — 学習データ
y_train: np.ndarray — 学習ラベル
X_test: np.ndarray — テストデータ
base_models: list | None — ベースモデル
meta_model: classifier | None — メタモデル
n_folds: int — CV 分割数
"""
if base_models is None:
base_models = [
("rf", RandomForestClassifier(n_estimators=200, random_state=42)),
("gbm", GradientBoostingClassifier(n_estimators=200, random_state=42)),
("svm", SVC(probability=True, random_state=42)),
]
if meta_model is None:
meta_model = LogisticRegression(max_iter=1000, random_state=42)
kf = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=42)
n_classes = len(np.unique(y_train))
oof_preds = np.zeros((len(y_train), len(base_models) * n_classes))
test_preds = np.zeros((len(X_test), len(base_models) * n_classes))
for i, (name, model) in enumerate(base_models):
col_start = i * n_classes
col_end = (i + 1) * n_classes
test_fold_preds = np.zeros((len(X_test), n_classes, n_folds))
for fold, (train_idx, val_idx) in enumerate(kf.split(X_train, y_train)):
m = model.__class__(**model.get_params()).fit(
X_train[train_idx], y_train[train_idx])
oof_preds[val_idx, col_start:col_end] = m.predict_proba(
X_train[val_idx])
test_fold_preds[:, :, fold] = m.predict_proba(X_test)
test_preds[:, col_start:col_end] = test_fold_preds.mean(axis=2)
print(f" Stacking base: {name} done")
meta_model.fit(oof_preds, y_train)
final_pred = meta_model.predict(test_preds)
final_proba = meta_model.predict_proba(test_preds)
print(f"Stacking: {len(base_models)} base models → meta-model")
return final_pred, final_proba, meta_model
3. Voting アンサンブル
from sklearn.ensemble import VotingClassifier, VotingRegressor
def voting_ensemble(X, y, models=None, voting="soft",
cv=5, scoring="f1_macro"):
"""
Voting アンサンブル。
Parameters:
X: np.ndarray — 特徴量
y: np.ndarray — ラベル
models: list | None — (name, model) ペア
voting: str — "soft" / "hard"
cv: int — CV 分割数
scoring: str — 評価指標
"""
if models is None:
models = [
("rf", RandomForestClassifier(n_estimators=200, random_state=42)),
("gbm", GradientBoostingClassifier(n_estimators=200, random_state=42)),
("lr", LogisticRegression(max_iter=1000, random_state=42)),
]
results = []
for name, model in models:
scores = cross_val_score(model, X, y, cv=cv, scoring=scoring)
results.append({"model": name, "mean": scores.mean(), "std": scores.std()})
vc = VotingClassifier(estimators=models, voting=voting)
scores = cross_val_score(vc, X, y, cv=cv, scoring=scoring)
results.append({"model": f"Voting({voting})",
"mean": scores.mean(), "std": scores.std()})
df = pd.DataFrame(results).sort_values("mean", ascending=False)
print(f"Voting ensemble: {scoring} = {scores.mean():.4f} ± {scores.std():.4f}")
return df
4. アンサンブル多様性評価
def ensemble_diversity(models, X, y):
"""
アンサンブル多様性 (Q-statistic / Disagreement)。
Parameters:
models: list — 学習済みモデルリスト
X: np.ndarray — 評価データ
y: np.ndarray — 真ラベル
"""
predictions = np.array([m.predict(X) for m in models])
n_models = len(models)
correct = (predictions == y).astype(int)
q_stats = []
disagree_rates = []
for i in range(n_models):
for j in range(i + 1, n_models):
n11 = np.sum((correct[i] == 1) & (correct[j] == 1))
n00 = np.sum((correct[i] == 0) & (correct[j] == 0))
n10 = np.sum((correct[i] == 1) & (correct[j] == 0))
n01 = np.sum((correct[i] == 0) & (correct[j] == 1))
denom = n11 * n00 - n10 * n01
numer = n11 * n00 + n10 * n01
q = denom / numer if numer != 0 else 0
q_stats.append(q)
disagree_rates.append((n10 + n01) / len(y))
result = {
"mean_q_statistic": round(np.mean(q_stats), 4),
"mean_disagreement": round(np.mean(disagree_rates), 4),
"n_models": n_models,
}
print(f"Diversity: Q={result['mean_q_statistic']:.3f}, "
f"Disagree={result['mean_disagreement']:.3f}")
return result
パイプライン統合
automl → ensemble-methods → uncertainty-quantification
(モデル選択) (アンサンブル) (不確実性定量化)
│ │ ↓
feature-importance ┘ explainable-ai
(特徴量重要度) (説明可能 AI)
パイプライン出力
| ファイル | 説明 | 次スキル |
|---|
stacking_meta.pkl | Stacking メタモデル | → 予測 |
boosting_comparison.csv | ブースティング比較 | → レポート |
ensemble_diversity.json | 多様性指標 | → モデル改善 |
ToolUniverse 連携
| TU Key | ツール名 | 連携内容 |
|---|
openml | OpenML | アンサンブル手法ベンチマーク参照 |
