| |
| from __future__ import annotations |
|
|
| from sklearn.ensemble import ( |
| ExtraTreesRegressor, |
| RandomForestRegressor, |
| GradientBoostingRegressor, |
| StackingRegressor, |
| ) |
|
|
| |
| try: |
| from xgboost import XGBRegressor |
| HAS_XGB = True |
| except Exception: |
| HAS_XGB = False |
|
|
|
|
| def build_sm2_stacking(random_state: int = 42) -> StackingRegressor: |
| """ |
| SM2-style stacking: |
| Base: ETR, RFR, GBR, (XGBR if available) |
| Meta: GBR |
| """ |
| base_estimators = [ |
| ("etr", ExtraTreesRegressor(n_estimators=1000, random_state=random_state, n_jobs=-1)), |
| ("rfr", RandomForestRegressor(n_estimators=1000, random_state=random_state, n_jobs=-1)), |
| ("gbr", GradientBoostingRegressor(random_state=random_state)), |
| ] |
|
|
| if HAS_XGB: |
| base_estimators.append( |
| ("xgbr", XGBRegressor( |
| n_estimators=100, |
| max_depth=6, |
| learning_rate=0.1, |
| subsample=0.9, |
| reg_lambda=1.0, |
| random_state=random_state, |
| n_jobs=-1, |
| tree_method="hist", |
| )) |
| ) |
|
|
| meta = GradientBoostingRegressor(random_state=random_state) |
|
|
| return StackingRegressor( |
| estimators=base_estimators, |
| final_estimator=meta, |
| passthrough=True, |
| n_jobs=-1, |
| ) |
|
|
