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c289d87 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | from __future__ import annotations
from dataclasses import dataclass
from typing import Dict, List, Optional
import numpy as np
from sklearn.ensemble import RandomForestRegressor
try:
from xgboost import XGBRegressor
except Exception: # pragma: no cover
XGBRegressor = None # type: ignore[assignment]
@dataclass
class SurrogateConfig:
prefer_xgboost: bool = True
random_state: int = 42
n_estimators: int = 200
min_train_samples: int = 8
max_depth_small: int = 3
max_depth_large: int = 6
class SurrogateModel:
"""Surrogate model with explicit missing-feature masking support."""
def __init__(self, config: Optional[SurrogateConfig] = None):
self.config = config or SurrogateConfig()
self.model = None
self.backend = "uninitialized"
self.feature_names: List[str] = []
self.mask_names: List[str] = []
self._median: np.ndarray | None = None
self._iqr: np.ndarray | None = None
self._train_x: np.ndarray | None = None
self._train_y: np.ndarray | None = None
self.last_fit_stats: Dict[str, float] = {}
def _build_model(self, n_samples: int):
max_depth = self.config.max_depth_small if n_samples < 50 else self.config.max_depth_large
if self.config.prefer_xgboost and XGBRegressor is not None:
self.backend = "xgboost"
return XGBRegressor(
n_estimators=self.config.n_estimators,
max_depth=max_depth,
learning_rate=0.05,
subsample=0.9,
colsample_bytree=0.9,
reg_alpha=0.1,
reg_lambda=1.0,
random_state=self.config.random_state,
)
self.backend = "random_forest"
return RandomForestRegressor(
n_estimators=max(100, self.config.n_estimators),
max_depth=max_depth,
random_state=self.config.random_state,
n_jobs=-1,
)
def _fit_normalizer(self, x: np.ndarray, m: np.ndarray) -> None:
med = []
iqr = []
for j in range(x.shape[1]):
vals = x[m[:, j] > 0.5, j]
if vals.size == 0:
med.append(0.0)
iqr.append(1.0)
continue
q1 = float(np.quantile(vals, 0.25))
q3 = float(np.quantile(vals, 0.75))
inter = max(1e-6, q3 - q1)
med.append(float(np.median(vals)))
iqr.append(inter)
self._median = np.asarray(med, dtype=float)
self._iqr = np.asarray(iqr, dtype=float)
def _transform(self, x: np.ndarray, m: np.ndarray, fit: bool = False) -> np.ndarray:
x = np.asarray(x, dtype=float)
m = np.asarray(m, dtype=float)
if fit or self._median is None or self._iqr is None:
self._fit_normalizer(x, m)
assert self._median is not None and self._iqr is not None
x_filled = np.where(np.isfinite(x), x, np.nan)
for j in range(x.shape[1]):
col = x_filled[:, j]
missing = ~np.isfinite(col)
col[missing] = self._median[j]
x_filled[:, j] = (col - self._median[j]) / self._iqr[j]
# Explicit missingness information: concatenate mask vector.
return np.concatenate([x_filled, m], axis=1)
def fit(
self,
features: np.ndarray,
masks: np.ndarray,
y: np.ndarray,
feature_names: List[str],
mask_names: List[str],
) -> Dict[str, float]:
y = np.asarray(y, dtype=float)
valid = np.isfinite(y)
n_samples = int(np.sum(valid))
self.feature_names = list(feature_names)
self.mask_names = list(mask_names)
if n_samples < self.config.min_train_samples:
self.model = None
self.backend = "warmup"
self.last_fit_stats = {
"n_train": float(n_samples),
"train_mae": np.nan,
"val_mae": np.nan,
"instability_ratio": 1.0,
}
return self.last_fit_stats
x = np.asarray(features, dtype=float)[valid]
m = np.asarray(masks, dtype=float)[valid]
yv = y[valid]
x_trans = self._transform(x, m, fit=True)
self._train_x = x_trans
self._train_y = yv
self.model = self._build_model(n_samples=n_samples)
self.model.fit(x_trans, yv)
train_pred = self.model.predict(x_trans)
train_mae = float(np.mean(np.abs(train_pred - yv)))
# Simple rolling validation proxy.
if n_samples >= 12:
split = int(max(8, n_samples * 0.8))
x_tr, x_val = x_trans[:split], x_trans[split:]
y_tr, y_val = yv[:split], yv[split:]
val_model = self._build_model(n_samples=split)
val_model.fit(x_tr, y_tr)
val_pred = val_model.predict(x_val)
val_mae = float(np.mean(np.abs(val_pred - y_val))) if y_val.size else train_mae
else:
val_mae = train_mae
instability = float((val_mae + 1e-6) / (train_mae + 1e-6))
self.last_fit_stats = {
"n_train": float(n_samples),
"train_mae": train_mae,
"val_mae": val_mae,
"instability_ratio": instability,
}
return self.last_fit_stats
def _rf_uncertainty(self, x: np.ndarray) -> np.ndarray:
assert self.model is not None
if not hasattr(self.model, "estimators_"):
return np.full(x.shape[0], float(np.std(self._train_y)) if self._train_y is not None else 1.0)
preds = np.vstack([tree.predict(x) for tree in self.model.estimators_])
return np.std(preds, axis=0)
def predict_bundle(self, features: np.ndarray, masks: np.ndarray, topk_threshold: float | None = None) -> Dict[str, np.ndarray]:
n = features.shape[0]
if self.model is None:
return {
"expected_score": np.zeros(n, dtype=float),
"topk_probability": np.full(n, 0.5, dtype=float),
"uncertainty": np.full(n, 1.0, dtype=float),
}
x = self._transform(np.asarray(features, dtype=float), np.asarray(masks, dtype=float), fit=False)
expected = self.model.predict(x)
if self.backend == "random_forest":
uncertainty = self._rf_uncertainty(x)
else:
residual_scale = (
float(np.std(self._train_y - self.model.predict(self._train_x)))
if self._train_x is not None and self._train_y is not None
else 1.0
)
uncertainty = np.full(expected.shape[0], max(1e-3, residual_scale))
if topk_threshold is None:
topk_threshold = float(np.quantile(self._train_y, 0.8)) if self._train_y is not None else 0.0
z = (expected - topk_threshold) / (uncertainty + 1e-6)
topk_prob = 1.0 / (1.0 + np.exp(-z))
return {
"expected_score": expected.astype(float),
"topk_probability": topk_prob.astype(float),
"uncertainty": uncertainty.astype(float),
}
def feature_importance(self) -> Dict[str, float]:
if self.model is None or not hasattr(self.model, "feature_importances_"):
return {}
names = self.feature_names + self.mask_names
importances = np.asarray(self.model.feature_importances_, dtype=float)
if importances.size != len(names):
return {}
return {name: float(value) for name, value in zip(names, importances)}
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