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21c7db9 | 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 | """Dosing supervised training placeholder."""
from __future__ import annotations
import pickle
from pathlib import Path
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.multioutput import MultiOutputRegressor
from app.common.enums import Difficulty
from app.models.dosing.dose_policy_features import build_dose_features
from app.simulator.patient_generator import generate_patient_profile
def train_dosing_surrogate(dataset_size: int) -> dict[str, float | str]:
feature_rows: list[list[float]] = []
target_rows: list[list[float]] = []
for i in range(dataset_size):
difficulty = Difficulty.HARD if i % 2 == 0 else Difficulty.MEDIUM
patient = generate_patient_profile(seed=5000 + i, difficulty=difficulty)
drug = patient.medications[0].drug if patient.medications else "warfarin_like"
feats = build_dose_features(patient, drug)
organ = feats.get("organ_stress", 0.0)
interaction = feats.get("interaction_load", 0.0)
adherence = feats.get("adherence", 0.7)
target_attainment = max(0.0, min(1.0, 0.72 + adherence * 0.15 - interaction * 0.2))
toxicity = max(0.0, min(1.0, 0.15 + organ * 0.5 + interaction * 0.25))
underdose = max(0.0, min(1.0, 0.25 + (1.0 - adherence) * 0.35 + max(0.0, 0.4 - interaction) * 0.1))
measurement_need = max(toxicity, underdose)
feature_rows.append(list(feats.values()))
target_rows.append([target_attainment, toxicity, underdose, measurement_need])
x = np.array(feature_rows, dtype=float)
y = np.array(target_rows, dtype=float)
model = MultiOutputRegressor(RandomForestRegressor(n_estimators=80, random_state=42))
model.fit(x, y)
preds = model.predict(x)
mae = float(np.mean(np.abs(preds - y)))
artifact = {
"model": model,
"feature_keys": list(build_dose_features(generate_patient_profile(seed=1, difficulty=Difficulty.EASY), "warfarin_like").keys()),
"target_keys": ["target_attainment", "toxicity_proxy", "underdose_proxy", "measurement_need"],
}
path = Path("outputs/models/dose_model.pkl")
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("wb") as f:
pickle.dump(artifact, f)
return {
"dataset_size": float(dataset_size),
"status": "trained",
"train_mae": round(mae, 4),
"model_path": str(path),
}
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