File size: 2,060 Bytes
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 57 58 59 60 61 | """Tabular model 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.tabular.features import build_tabular_features
from app.models.tabular.risk_heads import predict_risk_heads
from app.simulator.patient_generator import generate_patient_profile
TARGET_KEYS = [
"ade_proxy",
"hospitalization_proxy",
"falls_proxy",
"destabilization_proxy",
"burden_proxy",
]
def train_tabular_model(dataset_size: int) -> dict[str, float | str]:
x_rows: list[list[float]] = []
y_rows: list[list[float]] = []
for i in range(dataset_size):
if i < dataset_size // 3:
difficulty = Difficulty.EASY
elif i < (dataset_size * 2) // 3:
difficulty = Difficulty.MEDIUM
else:
difficulty = Difficulty.HARD
patient = generate_patient_profile(seed=3000 + i, difficulty=difficulty)
features = build_tabular_features(patient)
targets = predict_risk_heads(features)
x_rows.append(list(features.values()))
y_rows.append([targets[k] for k in TARGET_KEYS])
x = np.array(x_rows, dtype=float)
y = np.array(y_rows, dtype=float)
model = MultiOutputRegressor(RandomForestRegressor(n_estimators=80, random_state=42))
model.fit(x, y)
predictions = model.predict(x)
mae = float(np.mean(np.abs(predictions - y)))
artifact = {"model": model, "feature_keys": list(build_tabular_features(generate_patient_profile(seed=1, difficulty=Difficulty.EASY)).keys()), "target_keys": TARGET_KEYS}
path = Path("outputs/models/tabular_risk.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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