"""TGNN — Temporal Graph Network for patient trajectory (Phase 2).

Architecture follows TRANS (IJCAI 2024) + TETGN improvements:
  - Temporal heterogeneous graph: nodes are (patient_snapshot at time t).
  - Each snapshot is connected to phenotypes/labs/genes valid at that time.
  - Trajectory-aware aggregation (TETGN) — message passing weighted by
    elapsed time and clinical-event type.
  - Multi-task heads: next phenotype, next complication, time-to-event
    (lung disease, hospitalization, etc.).

Trained on the snapshot chains stored in PatientSpace + Neo4j ClinicalSnapshot.
"""
from __future__ import annotations
import os
import logging
from typing import Optional

logger = logging.getLogger("gemeo.train.tgnn")

CKPT = os.path.join(os.path.dirname(__file__), "..", "artifacts", "tgnn_trajectory.pt")


async def predict(space, ckpt_path: str):
    """Inference path used by `gemeo.trajectory.predict`."""
    if not os.path.exists(ckpt_path):
        return None
    try:
        import torch  # noqa: F401
    except ImportError:
        return None
    # Load model, build patient temporal graph, forward, decode horizons.
    # Stub: defer to bootstrap.
    return None


def train(epochs: int = 60):
    """Run as `python -m gemeo.train.tgnn`."""
    logger.info("TGNN scaffold — needs longitudinal labels from PatientSpace snapshots")


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    train()