591 lines
20 KiB
Python
591 lines
20 KiB
Python
import json
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import math
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import os
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import random
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import re
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Sequence, Tuple
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import numpy as np
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import torch
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from torch.utils.data import DataLoader, Dataset, Subset, random_split
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try:
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from tqdm import tqdm as _tqdm
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except Exception: # pragma: no cover
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_tqdm = None
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from dataset import HealthDataset
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from losses import (
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DiscreteTimeCIFNLLLoss,
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ExponentialNLLLoss,
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PiecewiseExponentialCIFNLLLoss,
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)
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from model import DelphiFork, SapDelphi, SimpleHead
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DAYS_PER_YEAR = 365.25
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N_TECH_TOKENS = 2 # pad=0, DOA=1, diseases start at 2
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def _progress(iterable, *, enabled: bool, desc: str, total: Optional[int] = None):
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if enabled and _tqdm is not None:
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return _tqdm(iterable, desc=desc, total=total)
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return iterable
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def make_inference_dataloader_kwargs(
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device: torch.device,
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num_workers: int,
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) -> Dict[str, Any]:
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"""DataLoader kwargs tuned for inference throughput.
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Behavior/metrics are unchanged; this only impacts speed.
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"""
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use_cuda = device.type == "cuda" and torch.cuda.is_available()
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kwargs: Dict[str, Any] = {
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"pin_memory": bool(use_cuda),
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}
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if num_workers > 0:
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kwargs["persistent_workers"] = True
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# default prefetch is 2; set explicitly for clarity.
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kwargs["prefetch_factor"] = 2
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return kwargs
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# -------------------------
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# Config + determinism
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# -------------------------
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def _replace_nonstandard_json_numbers(text: str) -> str:
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# Python's json.dump writes Infinity/-Infinity/NaN for non-finite floats.
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# Replace bare tokens (not within quotes) with string placeholders.
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def repl(match: re.Match[str]) -> str:
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token = match.group(0)
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if token == "-Infinity":
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return '"__NINF__"'
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if token == "Infinity":
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return '"__INF__"'
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if token == "NaN":
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return '"__NAN__"'
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return token
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return re.sub(r'(?<![\w\."])(-Infinity|Infinity|NaN)(?![\w\."])', repl, text)
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def _restore_placeholders(obj: Any) -> Any:
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if isinstance(obj, dict):
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return {k: _restore_placeholders(v) for k, v in obj.items()}
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if isinstance(obj, list):
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return [_restore_placeholders(v) for v in obj]
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if obj == "__INF__":
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return float("inf")
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if obj == "__NINF__":
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return float("-inf")
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if obj == "__NAN__":
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return float("nan")
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return obj
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def load_train_config(run_dir: str) -> Dict[str, Any]:
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cfg_path = os.path.join(run_dir, "train_config.json")
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with open(cfg_path, "r", encoding="utf-8") as f:
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raw = f.read()
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raw = _replace_nonstandard_json_numbers(raw)
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cfg = json.loads(raw)
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cfg = _restore_placeholders(cfg)
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return cfg
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def seed_everything(seed: int) -> None:
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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def parse_float_list(values: Sequence[str]) -> List[float]:
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out: List[float] = []
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for v in values:
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s = str(v).strip().lower()
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if s in {"inf", "+inf", "infty", "infinity", "+infinity"}:
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out.append(float("inf"))
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elif s in {"-inf", "-infty", "-infinity"}:
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out.append(float("-inf"))
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else:
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out.append(float(v))
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return out
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# -------------------------
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# Dataset + split (match train.py)
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# -------------------------
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def build_dataset_from_config(cfg: Dict[str, Any]) -> HealthDataset:
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data_prefix = cfg["data_prefix"]
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full_cov = bool(cfg.get("full_cov", False))
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if full_cov:
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cov_list = None
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else:
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cov_list = ["bmi", "smoking", "alcohol"]
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dataset = HealthDataset(
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data_prefix=data_prefix,
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covariate_list=cov_list,
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)
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return dataset
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def get_test_subset(dataset: HealthDataset, cfg: Dict[str, Any]) -> Subset:
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n_total = len(dataset)
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train_ratio = float(cfg["train_ratio"])
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val_ratio = float(cfg["val_ratio"])
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seed = int(cfg["random_seed"])
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n_train = int(n_total * train_ratio)
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n_val = int(n_total * val_ratio)
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n_test = n_total - n_train - n_val
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_, _, test_subset = random_split(
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dataset,
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[n_train, n_val, n_test],
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generator=torch.Generator().manual_seed(seed),
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)
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return test_subset
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# -------------------------
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# Model + head + criterion (match train.py)
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# -------------------------
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def build_model_head_criterion(
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cfg: Dict[str, Any],
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dataset: HealthDataset,
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device: torch.device,
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) -> Tuple[torch.nn.Module, torch.nn.Module, torch.nn.Module]:
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loss_type = cfg["loss_type"]
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if loss_type == "exponential":
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criterion = ExponentialNLLLoss(lambda_reg=float(
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cfg.get("lambda_reg", 0.0))).to(device)
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out_dims = [dataset.n_disease]
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elif loss_type == "discrete_time_cif":
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bin_edges = [float(x) for x in cfg["bin_edges"]]
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criterion = DiscreteTimeCIFNLLLoss(
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bin_edges=bin_edges,
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lambda_reg=float(cfg.get("lambda_reg", 0.0)),
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).to(device)
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out_dims = [dataset.n_disease + 1, len(bin_edges)]
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elif loss_type == "pwe_cif":
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# training drops +inf for PWE
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raw_edges = [float(x) for x in cfg["bin_edges"]]
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pwe_edges = [float(x) for x in raw_edges if math.isfinite(float(x))]
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if len(pwe_edges) < 2:
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raise ValueError(
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"pwe_cif requires at least 2 finite bin edges (including 0). "
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f"Got bin_edges={raw_edges}"
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)
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if float(pwe_edges[0]) != 0.0:
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raise ValueError(
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f"pwe_cif requires bin_edges[0]==0.0; got {pwe_edges[0]}")
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criterion = PiecewiseExponentialCIFNLLLoss(
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bin_edges=pwe_edges,
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lambda_reg=float(cfg.get("lambda_reg", 0.0)),
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).to(device)
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n_bins = len(pwe_edges) - 1
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out_dims = [dataset.n_disease, n_bins]
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else:
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raise ValueError(f"Unsupported loss_type: {loss_type}")
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model_type = cfg["model_type"]
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if model_type == "delphi_fork":
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model = DelphiFork(
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n_disease=dataset.n_disease,
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n_tech_tokens=N_TECH_TOKENS,
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n_embd=int(cfg["n_embd"]),
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n_head=int(cfg["n_head"]),
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n_layer=int(cfg["n_layer"]),
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pdrop=float(cfg.get("pdrop", 0.0)),
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age_encoder_type=str(cfg.get("age_encoder", "sinusoidal")),
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n_cont=int(dataset.n_cont),
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n_cate=int(dataset.n_cate),
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cate_dims=list(dataset.cate_dims),
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).to(device)
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elif model_type == "sap_delphi":
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model = SapDelphi(
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n_disease=dataset.n_disease,
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n_tech_tokens=N_TECH_TOKENS,
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n_embd=int(cfg["n_embd"]),
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n_head=int(cfg["n_head"]),
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n_layer=int(cfg["n_layer"]),
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pdrop=float(cfg.get("pdrop", 0.0)),
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age_encoder_type=str(cfg.get("age_encoder", "sinusoidal")),
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n_cont=int(dataset.n_cont),
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n_cate=int(dataset.n_cate),
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cate_dims=list(dataset.cate_dims),
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pretrained_weights_path=str(
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cfg.get("pretrained_emd_path", "icd10_sapbert_embeddings.npy")),
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freeze_embeddings=True,
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).to(device)
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else:
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raise ValueError(f"Unsupported model_type: {model_type}")
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head = SimpleHead(
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n_embd=int(cfg["n_embd"]),
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out_dims=list(out_dims),
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).to(device)
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return model, head, criterion
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def load_checkpoint_into(
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run_dir: str,
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model: torch.nn.Module,
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head: torch.nn.Module,
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criterion: Optional[torch.nn.Module],
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device: torch.device,
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) -> Dict[str, Any]:
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ckpt_path = os.path.join(run_dir, "best_model.pt")
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ckpt = torch.load(ckpt_path, map_location=device)
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model.load_state_dict(ckpt["model_state_dict"], strict=True)
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head.load_state_dict(ckpt["head_state_dict"], strict=True)
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if criterion is not None and "criterion_state_dict" in ckpt:
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try:
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criterion.load_state_dict(
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ckpt["criterion_state_dict"], strict=False)
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except Exception:
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# Criterion state is not essential for inference.
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pass
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return ckpt
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# -------------------------
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# Evaluation record construction (event-driven)
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# -------------------------
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@dataclass(frozen=True)
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class EvalRecord:
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patient_idx: int
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patient_id: Any
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doa_days: float
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t0_days: float
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cutoff_pos: int # baseline position (inclusive)
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next_event_cause: Optional[int]
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next_event_dt_years: Optional[float]
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future_causes: np.ndarray # (E,) in [0..K-1]
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future_dt_years: np.ndarray # (E,) strictly > 0
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def _to_days(x_years: float) -> float:
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if math.isinf(float(x_years)):
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return float("inf")
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return float(x_years) * DAYS_PER_YEAR
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def build_event_driven_records(
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dataset: HealthDataset,
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subset: Subset,
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age_bins_years: Sequence[float],
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seed: int,
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show_progress: bool = False,
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) -> List[EvalRecord]:
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if len(age_bins_years) < 2:
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raise ValueError("age_bins must have at least 2 boundaries")
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age_bins_days = [_to_days(b) for b in age_bins_years]
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if any(age_bins_days[i] >= age_bins_days[i + 1] for i in range(len(age_bins_days) - 1)):
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raise ValueError("age_bins must be strictly increasing")
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rng = np.random.default_rng(seed)
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records: List[EvalRecord] = []
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# Subset.indices is deterministic from random_split
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indices = list(getattr(subset, "indices", range(len(subset))))
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# Speed: avoid calling dataset.__getitem__ for every patient here.
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# We only need DOA + event times/codes to create evaluation records.
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eps = 1e-6
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for patient_idx in _progress(
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indices,
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enabled=show_progress,
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desc="Building eval records",
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total=len(indices),
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):
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patient_id = dataset.patient_ids[patient_idx]
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doa_days = float(dataset._doa[patient_idx])
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raw_records = dataset.patient_events.get(patient_id, [])
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if raw_records:
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times = np.asarray([t for t, _ in raw_records], dtype=np.float64)
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codes = np.asarray([c for _, c in raw_records], dtype=np.int64)
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else:
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times = np.zeros((0,), dtype=np.float64)
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codes = np.zeros((0,), dtype=np.int64)
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# Mirror HealthDataset insertion logic exactly.
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insert_pos = int(np.searchsorted(times, doa_days, side="left"))
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times_ins = np.insert(times, insert_pos, doa_days)
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codes_ins = np.insert(codes, insert_pos, 1)
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is_disease = codes_ins >= N_TECH_TOKENS
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disease_times = times_ins[is_disease]
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for b in range(len(age_bins_days) - 1):
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lo = age_bins_days[b]
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hi = age_bins_days[b + 1]
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# Inclusion rule:
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# 1) DOA <= bin_upper
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if not (doa_days <= hi):
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continue
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# 2) at least one disease event within bin, and baseline must satisfy t0>=DOA
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in_bin = (disease_times >= lo) & (
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disease_times < hi) & (disease_times >= doa_days)
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cand_times = disease_times[in_bin]
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if cand_times.size == 0:
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continue
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t0_days = float(rng.choice(cand_times))
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# Baseline position (inclusive) in the *post-DOA-inserted* sequence.
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pos = np.flatnonzero(is_disease & np.isclose(
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times_ins, t0_days, rtol=0.0, atol=eps))
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if pos.size == 0:
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disease_pos = np.flatnonzero(is_disease)
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if disease_pos.size == 0:
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continue
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disease_times_full = times_ins[disease_pos]
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closest_idx = int(
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np.argmin(np.abs(disease_times_full - t0_days)))
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cutoff_pos = int(disease_pos[closest_idx])
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t0_days = float(disease_times_full[closest_idx])
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else:
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cutoff_pos = int(pos[0])
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# Future disease events strictly after t0
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future_mask = (times_ins > (t0_days + eps)) & is_disease
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future_pos = np.flatnonzero(future_mask)
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if future_pos.size == 0:
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next_cause = None
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next_dt_years = None
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future_causes = np.zeros((0,), dtype=np.int64)
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future_dt_years_arr = np.zeros((0,), dtype=np.float32)
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else:
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future_times_days = times_ins[future_pos]
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future_tokens = codes_ins[future_pos]
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future_causes = (future_tokens - N_TECH_TOKENS).astype(np.int64)
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future_dt_years_arr = (
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(future_times_days - t0_days) / DAYS_PER_YEAR).astype(np.float32)
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# next-event = minimal time > t0 (tie broken by earliest position)
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next_idx = int(np.argmin(future_times_days))
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next_cause = int(future_causes[next_idx])
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next_dt_years = float(future_dt_years_arr[next_idx])
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records.append(
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EvalRecord(
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patient_idx=int(patient_idx),
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patient_id=patient_id,
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doa_days=float(doa_days),
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t0_days=float(t0_days),
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cutoff_pos=int(cutoff_pos),
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next_event_cause=next_cause,
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next_event_dt_years=next_dt_years,
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future_causes=future_causes,
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future_dt_years=future_dt_years_arr,
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)
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)
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return records
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class EvalRecordDataset(Dataset):
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def __init__(self, base_dataset: HealthDataset, records: Sequence[EvalRecord]):
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self.base = base_dataset
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self.records = list(records)
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self._cache: Dict[int, Tuple[torch.Tensor,
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torch.Tensor, torch.Tensor, torch.Tensor, int]] = {}
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self._cache_order: List[int] = []
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self._cache_max = 2048
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def __len__(self) -> int:
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return len(self.records)
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def __getitem__(self, idx: int):
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rec = self.records[idx]
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cached = self._cache.get(rec.patient_idx)
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if cached is None:
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event_seq, time_seq, cont, cate, sex = self.base[rec.patient_idx]
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cached = (event_seq, time_seq, cont, cate, int(sex))
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self._cache[rec.patient_idx] = cached
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self._cache_order.append(rec.patient_idx)
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if len(self._cache_order) > self._cache_max:
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drop = self._cache_order.pop(0)
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self._cache.pop(drop, None)
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else:
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event_seq, time_seq, cont, cate, sex = cached
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cutoff = rec.cutoff_pos + 1
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event_seq = event_seq[:cutoff]
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time_seq = time_seq[:cutoff]
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baseline_pos = rec.cutoff_pos # same index in truncated sequence
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return event_seq, time_seq, cont, cate, sex, baseline_pos
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def eval_collate_fn(batch):
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from torch.nn.utils.rnn import pad_sequence
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event_seqs, time_seqs, cont_feats, cate_feats, sexes, baseline_pos = zip(
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*batch)
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event_batch = pad_sequence(event_seqs, batch_first=True, padding_value=0)
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time_batch = pad_sequence(
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time_seqs, batch_first=True, padding_value=36525.0)
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cont_batch = torch.stack(cont_feats, dim=0).unsqueeze(1)
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cate_batch = torch.stack(cate_feats, dim=0).unsqueeze(1)
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sex_batch = torch.tensor(sexes, dtype=torch.long)
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baseline_pos = torch.tensor(baseline_pos, dtype=torch.long)
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return event_batch, time_batch, cont_batch, cate_batch, sex_batch, baseline_pos
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# -------------------------
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# Inference utilities
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# -------------------------
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def predict_cifs(
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model: torch.nn.Module,
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head: torch.nn.Module,
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criterion: torch.nn.Module,
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loader: DataLoader,
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taus_years: Sequence[float],
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device: torch.device,
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show_progress: bool = False,
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progress_desc: str = "Inference",
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) -> np.ndarray:
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model.eval()
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head.eval()
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taus_t = torch.tensor(list(taus_years), dtype=torch.float32, device=device)
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all_out: List[np.ndarray] = []
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with torch.no_grad():
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for batch in _progress(
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loader,
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enabled=show_progress,
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desc=progress_desc,
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total=len(loader) if hasattr(loader, "__len__") else None,
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):
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event_seq, time_seq, cont, cate, sex, baseline_pos = batch
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event_seq = event_seq.to(device, non_blocking=True)
|
|
time_seq = time_seq.to(device, non_blocking=True)
|
|
cont = cont.to(device, non_blocking=True)
|
|
cate = cate.to(device, non_blocking=True)
|
|
sex = sex.to(device, non_blocking=True)
|
|
baseline_pos = baseline_pos.to(device, non_blocking=True)
|
|
|
|
h = model(event_seq, time_seq, sex, cont, cate)
|
|
b_idx = torch.arange(h.size(0), device=device)
|
|
c = h[b_idx, baseline_pos]
|
|
logits = head(c)
|
|
|
|
cifs = criterion.calculate_cifs(logits, taus_t)
|
|
out = cifs.detach().cpu().numpy()
|
|
all_out.append(out)
|
|
|
|
return np.concatenate(all_out, axis=0) if all_out else np.zeros((0,))
|
|
|
|
|
|
def flatten_future_events(
|
|
records: Sequence[EvalRecord],
|
|
n_causes: int,
|
|
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
|
"""Flatten (record_idx, cause, dt_years) across all future events.
|
|
|
|
Used to build horizon labels via vectorized masking + scatter.
|
|
"""
|
|
rec_idx_parts: List[np.ndarray] = []
|
|
cause_parts: List[np.ndarray] = []
|
|
dt_parts: List[np.ndarray] = []
|
|
|
|
for i, r in enumerate(records):
|
|
if r.future_causes.size == 0:
|
|
continue
|
|
causes = r.future_causes
|
|
dts = r.future_dt_years
|
|
# Keep only valid cause ids.
|
|
m = (causes >= 0) & (causes < n_causes)
|
|
if not np.any(m):
|
|
continue
|
|
causes = causes[m].astype(np.int64, copy=False)
|
|
dts = dts[m].astype(np.float32, copy=False)
|
|
rec_idx_parts.append(np.full((causes.size,), i, dtype=np.int32))
|
|
cause_parts.append(causes)
|
|
dt_parts.append(dts)
|
|
|
|
if not rec_idx_parts:
|
|
return (
|
|
np.zeros((0,), dtype=np.int32),
|
|
np.zeros((0,), dtype=np.int64),
|
|
np.zeros((0,), dtype=np.float32),
|
|
)
|
|
|
|
return (
|
|
np.concatenate(rec_idx_parts, axis=0),
|
|
np.concatenate(cause_parts, axis=0),
|
|
np.concatenate(dt_parts, axis=0),
|
|
)
|
|
|
|
|
|
# -------------------------
|
|
# Metrics helpers
|
|
# -------------------------
|
|
|
|
def roc_auc_ovr(y_true: np.ndarray, y_score: np.ndarray) -> float:
|
|
"""Binary ROC AUC with tie-aware average ranks.
|
|
|
|
Returns NaN if y_true has no positives or no negatives.
|
|
"""
|
|
y_true = np.asarray(y_true).astype(np.int32)
|
|
y_score = np.asarray(y_score).astype(np.float64)
|
|
|
|
n_pos = int(y_true.sum())
|
|
n = int(y_true.size)
|
|
n_neg = n - n_pos
|
|
if n_pos == 0 or n_neg == 0:
|
|
return float("nan")
|
|
|
|
order = np.argsort(y_score, kind="mergesort")
|
|
scores_sorted = y_score[order]
|
|
y_sorted = y_true[order]
|
|
|
|
ranks = np.empty(n, dtype=np.float64)
|
|
i = 0
|
|
while i < n:
|
|
j = i + 1
|
|
while j < n and scores_sorted[j] == scores_sorted[i]:
|
|
j += 1
|
|
# average rank for ties, ranks are 1..n
|
|
avg_rank = 0.5 * (i + 1 + j)
|
|
ranks[i:j] = avg_rank
|
|
i = j
|
|
|
|
sum_ranks_pos = float((ranks * y_sorted).sum())
|
|
auc = (sum_ranks_pos - n_pos * (n_pos + 1) / 2.0) / (n_pos * n_neg)
|
|
return float(auc)
|
|
|
|
|
|
def topk_indices(scores: np.ndarray, k: int) -> np.ndarray:
|
|
"""Return indices of top-k scores per row (descending)."""
|
|
if k <= 0:
|
|
raise ValueError("k must be positive")
|
|
n, K = scores.shape
|
|
k = min(k, K)
|
|
# argpartition gives arbitrary order within topk; sort those by score
|
|
part = np.argpartition(-scores, kth=k - 1, axis=1)[:, :k]
|
|
part_scores = np.take_along_axis(scores, part, axis=1)
|
|
order = np.argsort(-part_scores, axis=1, kind="mergesort")
|
|
return np.take_along_axis(part, order, axis=1)
|