512 lines
18 KiB
Python
512 lines
18 KiB
Python
"""Shared landmark evaluation helpers for attribution scripts."""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Sequence
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import numpy as np
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import pandas as pd
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import torch
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from torch.nn.utils.rnn import pad_sequence
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from torch.utils.data import Dataset
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from dataset import HealthDataset
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from eval_data import load_sequence_eval_dataset
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from evaluate_auc_v2 import (
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LandmarkDataset,
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build_model_from_dataset,
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cfg_get,
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make_eval_indices,
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)
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from models import DeepHealth
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from readouts import build_readout
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from targets import CHECKUP_IDX, NO_EVENT_IDX, PAD_IDX
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from train_util import load_eid_file, load_extra_info_types_file
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SPECIAL_TOKENS = {PAD_IDX, CHECKUP_IDX, NO_EVENT_IDX}
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def parse_int_list(value: Any) -> Optional[List[int]]:
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if value is None:
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return None
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if isinstance(value, (list, tuple, np.ndarray)):
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return [int(x) for x in value]
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text = str(value).strip()
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if text == "":
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return None
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if text.startswith("["):
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values = json.loads(text)
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if not isinstance(values, list):
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raise ValueError(f"Expected a JSON list, got {type(values).__name__}")
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return [int(x) for x in values]
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return [int(x.strip()) for x in text.split(",") if x.strip()]
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def load_extra_info_types(value: Any) -> Optional[List[int]]:
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if value is None:
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return None
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text = str(value)
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path = Path(text)
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if path.exists():
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return load_extra_info_types_file(text)
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return parse_int_list(value)
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def make_landmark_ages(start: float, stop: float, step: float) -> np.ndarray:
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if step <= 0:
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raise ValueError("landmark_step must be positive")
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if stop < start:
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raise ValueError("landmark_stop must be >= landmark_start")
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# Include stop when it lands on the grid, e.g. 40,45,...,80.
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return np.arange(start, stop + step * 0.5, step, dtype=np.float32)
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def build_first_occurrence_maps_for_landmarks(
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dataset: HealthDataset,
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subset_indices: np.ndarray,
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) -> Dict[int, tuple[np.ndarray, np.ndarray]]:
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first_lists: Dict[int, list[tuple[int, float]]] = {}
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for patient_id, dataset_index in enumerate(np.asarray(subset_indices, dtype=np.int64).tolist()):
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s = dataset.samples[int(dataset_index)]
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seq_event = np.asarray(s["event_seq"], dtype=np.int64)
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seq_time = np.asarray(s["time_seq"], dtype=np.float32)
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tgt_event = np.asarray(s["target_event_seq"], dtype=np.int64)
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tgt_time = np.asarray(s["target_time_seq"], dtype=np.float32)
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if seq_event.size == 0 or tgt_event.size == 0:
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continue
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full_event = np.concatenate([seq_event, tgt_event[-1:]])
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full_time = np.concatenate([seq_time, tgt_time[-1:]])
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uniq_tokens, first_idx = np.unique(full_event, return_index=True)
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for token, idx in zip(uniq_tokens.tolist(), first_idx.tolist()):
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token = int(token)
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if token in SPECIAL_TOKENS:
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continue
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first_lists.setdefault(token, []).append((patient_id, float(full_time[int(idx)])))
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return {
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int(token): (
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np.asarray([p for p, _ in pairs], dtype=np.int32),
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np.asarray([t for _, t in pairs], dtype=np.float32),
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)
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for token, pairs in first_lists.items()
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if pairs
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}
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def normalize_eval_split(args: argparse.Namespace, cfg: Dict[str, Any]) -> str:
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eval_split = str(cfg_get(args, cfg, "eval_split", "test")).lower()
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if eval_split in {"valid", "validation"}:
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return "val"
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if eval_split not in {"train", "val", "test", "all"}:
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raise ValueError(f"Unsupported eval_split={eval_split!r}")
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return eval_split
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def load_eval_sequence_dataset(
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args: argparse.Namespace,
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cfg: Dict[str, Any],
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) -> tuple[Any, np.ndarray, str, str]:
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eval_split = normalize_eval_split(args, cfg)
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model_target_mode = str(cfg.get("model_target_mode", "next_token")).lower()
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data_prefix = str(cfg.get("data_prefix", "ukb"))
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labels_file = str(cfg.get("labels_file", "labels.csv"))
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no_event_interval_years = float(cfg.get("no_event_interval_years", 5.0))
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include_no_event_in_uts_target = bool(cfg.get("include_no_event_in_uts_target", False))
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extra_info_types = load_extra_info_types(args.extra_info_types)
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if extra_info_types is None:
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extra_info_types = parse_int_list(cfg.get("extra_info_types", None))
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print("Loading one sequence eval dataset...")
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dataset = load_sequence_eval_dataset(
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model_target_mode=model_target_mode,
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data_prefix=data_prefix,
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labels_file=labels_file,
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no_event_interval_years=no_event_interval_years,
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include_no_event_in_uts_target=include_no_event_in_uts_target,
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min_history_events=int(cfg.get("all_future_min_history_events", 1)),
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min_future_events=int(cfg.get("all_future_min_future_events", 1)),
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extra_info_types=extra_info_types,
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)
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train_eid_file = cfg_get(args, cfg, "train_eid_file", "ukb_train_eid.csv")
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val_eid_file = cfg_get(args, cfg, "val_eid_file", "ukb_val_eid.csv")
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test_eid_file = cfg_get(args, cfg, "test_eid_file", "ukb_test_eid.csv")
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split_files_exist = all(
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Path(str(path)).exists()
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for path in (train_eid_file, val_eid_file, test_eid_file)
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)
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if eval_split != "all" and split_files_exist:
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split_files = {
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"train": train_eid_file,
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"val": val_eid_file,
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"test": test_eid_file,
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}
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selected_eids = load_eid_file(split_files[eval_split])
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out = np.asarray(
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[
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idx
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for idx, sample in enumerate(dataset.samples)
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if int(sample["eid"]) in selected_eids
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],
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dtype=np.int64,
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)
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if out.size == 0:
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raise ValueError(
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f"No samples found for eval_split={eval_split!r} using {split_files[eval_split]}"
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)
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split_source = "eid_files"
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else:
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if eval_split == "all":
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out = np.arange(len(dataset.samples), dtype=np.int64)
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split_source = "all"
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else:
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out = make_eval_indices(dataset, args, cfg)
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split_source = "ratio_split"
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subset_size = cfg_get(args, cfg, "dataset_subset_size", None)
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if subset_size is not None and int(subset_size) > 0:
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out = out[: int(subset_size)]
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return dataset, np.asarray(out, dtype=np.int64), eval_split, split_source
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def load_organ_groups(
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path: Path,
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*,
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vocab_size: int,
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) -> tuple[dict[str, list[int]], dict[str, str], dict[int, str]]:
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table = pd.read_csv(path)
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required = {"token_id", "organ_system", "organ_system_label", "is_death"}
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missing = required - set(table.columns)
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if missing:
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raise ValueError(f"{path} is missing columns: {sorted(missing)}")
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death_idx = int(vocab_size) - 1
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groups: dict[str, list[int]] = {}
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labels: dict[str, str] = {}
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token_to_group: dict[int, str] = {}
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for row in table.itertuples(index=False):
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token = int(getattr(row, "token_id"))
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if token in SPECIAL_TOKENS or token == death_idx:
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continue
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if token < 0 or token >= int(vocab_size):
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continue
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if int(getattr(row, "is_death")) == 1:
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continue
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group = str(getattr(row, "organ_system"))
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label = str(getattr(row, "organ_system_label"))
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groups.setdefault(group, []).append(token)
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labels[group] = label
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token_to_group[token] = group
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groups = {k: sorted(set(v)) for k, v in groups.items() if v}
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return groups, labels, token_to_group
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class IndexedLandmarkDataset(Dataset):
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def __init__(self, base: LandmarkDataset) -> None:
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self.base = base
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def __len__(self) -> int:
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return len(self.base)
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def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
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item = dict(self.base[idx])
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item["row_idx"] = torch.tensor(int(idx), dtype=torch.long)
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return item
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def collate_indexed_landmark_fn(batch: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
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event_seq = pad_sequence(
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[x["event_seq"] for x in batch], batch_first=True, padding_value=PAD_IDX
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)
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time_seq = pad_sequence(
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[x["time_seq"] for x in batch], batch_first=True, padding_value=0.0
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)
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readout_mask = pad_sequence(
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[x["readout_mask"] for x in batch], batch_first=True, padding_value=False
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)
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other_type = pad_sequence(
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[x["other_type"] for x in batch], batch_first=True, padding_value=0
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)
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other_value = pad_sequence(
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[x["other_value"] for x in batch], batch_first=True, padding_value=0.0
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)
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other_value_kind = pad_sequence(
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[x["other_value_kind"] for x in batch], batch_first=True, padding_value=0
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)
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other_time = pad_sequence(
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[x["other_time"] for x in batch], batch_first=True, padding_value=0.0
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)
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return {
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"event_seq": event_seq,
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"time_seq": time_seq,
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"padding_mask": event_seq > PAD_IDX,
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"readout_mask": readout_mask,
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"sex": torch.stack([x["sex"] for x in batch]),
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"other_type": other_type,
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"other_value": other_value,
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"other_value_kind": other_value_kind,
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"other_time": other_time,
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"landmark_pos": torch.stack([x["landmark_pos"] for x in batch]),
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"t_query": torch.stack([x["t_query"] for x in batch]),
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"patient_id": torch.stack([x["patient_id"] for x in batch]),
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"landmark_age": torch.stack([x["landmark_age"] for x in batch]),
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"followup_end_time": torch.stack([x["followup_end_time"] for x in batch]),
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"death_time": torch.stack([x["death_time"] for x in batch]),
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"row_idx": torch.stack([x["row_idx"] for x in batch]),
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}
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def build_group_ablated_slice(
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batch: Dict[str, torch.Tensor],
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token_ids: Sequence[int],
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row_indices: torch.Tensor,
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) -> Dict[str, torch.Tensor]:
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"""Build one fixed-width ablated slice without rebuilding variable-length rows."""
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event_seq = batch["event_seq"]
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out: Dict[str, torch.Tensor] = {}
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out["event_seq"] = event_seq[row_indices].clone()
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out["time_seq"] = batch["time_seq"][row_indices]
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out["readout_mask"] = batch["readout_mask"][row_indices].clone()
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out["padding_mask"] = batch["padding_mask"][row_indices].bool().clone()
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out["landmark_pos"] = batch["landmark_pos"][row_indices].clone()
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seq_len = int(event_seq.shape[1])
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positions = torch.arange(seq_len, device=event_seq.device)[None, :]
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ids = torch.as_tensor(token_ids, dtype=event_seq.dtype, device=event_seq.device)
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remove = torch.isin(out["event_seq"], ids) & out["padding_mask"]
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out["event_seq"] = torch.where(
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remove,
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torch.full_like(out["event_seq"], PAD_IDX),
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out["event_seq"],
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)
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out["padding_mask"] &= ~remove
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out["readout_mask"] &= ~remove
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has_valid = out["padding_mask"].any(dim=1)
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if not bool(has_valid.all().item()):
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empty_rows = torch.nonzero(~has_valid, as_tuple=False).flatten()
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out["event_seq"][empty_rows, 0] = CHECKUP_IDX
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out["time_seq"][empty_rows, 0] = batch["t_query"][row_indices[empty_rows]].to(
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dtype=out["time_seq"].dtype
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)
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out["padding_mask"][empty_rows, 0] = True
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out["readout_mask"][empty_rows, 0] = True
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out["landmark_pos"][empty_rows] = 0
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has_readout = out["readout_mask"].any(dim=1)
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if not bool(has_readout.all().item()):
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rows = torch.nonzero(~has_readout, as_tuple=False).flatten()
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local_valid = out["padding_mask"][rows]
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last_pos = torch.where(
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local_valid,
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positions.expand(local_valid.shape[0], -1),
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torch.zeros_like(positions.expand(local_valid.shape[0], -1)),
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).amax(dim=1)
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out["readout_mask"][rows] = False
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out["readout_mask"][rows, last_pos] = True
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out["landmark_pos"][rows] = last_pos.to(dtype=out["landmark_pos"].dtype)
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repeated_keys = (
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"sex",
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"other_type",
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"other_value",
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"other_value_kind",
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"other_time",
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"t_query",
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"patient_id",
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"landmark_age",
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"followup_end_time",
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"death_time",
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"row_idx",
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)
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for key in repeated_keys:
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out[key] = batch[key][row_indices]
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return out
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def concat_tensor_batches(chunks: Sequence[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
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return {
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key: torch.cat([chunk[key] for chunk in chunks], dim=0)
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for key in chunks[0]
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}
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def iter_group_ablated_batches(
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batch: Dict[str, torch.Tensor],
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group_names: Sequence[str],
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organ_groups: dict[str, list[int]],
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occurred: torch.Tensor,
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max_batch_size: int,
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):
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"""Yield ablated chunks as soon as enough rows are available for a forward pass."""
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pending_batches: list[Dict[str, torch.Tensor]] = []
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pending_groups: list[str] = []
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pending_rows: list[int] = []
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pending_n = 0
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for group in group_names:
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ids = torch.as_tensor(organ_groups[group], dtype=torch.long, device=occurred.device)
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if ids.numel() == 0:
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continue
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active_rows = torch.nonzero(occurred[:, ids].any(dim=1), as_tuple=False).flatten()
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if active_rows.numel() == 0:
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continue
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row_offset = 0
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while row_offset < int(active_rows.numel()):
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capacity = int(max_batch_size) - pending_n
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row_stop = min(int(active_rows.numel()), row_offset + capacity)
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row_indices = active_rows[row_offset:row_stop].to(device=batch["event_seq"].device)
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chunk = build_group_ablated_slice(
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batch=batch,
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token_ids=organ_groups[group],
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row_indices=row_indices,
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)
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chunk_n = int(row_indices.numel())
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pending_batches.append(chunk)
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pending_groups.extend([group] * chunk_n)
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pending_rows.extend(int(x) for x in row_indices.detach().cpu().tolist())
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pending_n += chunk_n
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row_offset = row_stop
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if pending_n >= int(max_batch_size):
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yield concat_tensor_batches(pending_batches), pending_groups, pending_rows
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pending_batches = []
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pending_groups = []
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pending_rows = []
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pending_n = 0
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if pending_batches:
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yield concat_tensor_batches(pending_batches), pending_groups, pending_rows
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@torch.no_grad()
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def infer_landmark_hidden(
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*,
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model: DeepHealth,
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batch: Dict[str, torch.Tensor],
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device: torch.device,
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model_target_mode: str,
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readout_name: str,
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readout_reduce: str,
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) -> torch.Tensor:
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batch_dev = {
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k: (v.to(device, non_blocking=True) if isinstance(v, torch.Tensor) else v)
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for k, v in batch.items()
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}
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if model_target_mode == "all_future":
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return model(
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event_seq=batch_dev["event_seq"].long(),
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time_seq=batch_dev["time_seq"].float(),
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sex=batch_dev["sex"].long(),
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padding_mask=batch_dev["padding_mask"].bool(),
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t_query=batch_dev["t_query"].float(),
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other_type=batch_dev["other_type"].long(),
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other_value=batch_dev["other_value"].float(),
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other_value_kind=batch_dev["other_value_kind"].long(),
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other_time=batch_dev["other_time"].float(),
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target_mode="all_future",
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)
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hidden = model(
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event_seq=batch_dev["event_seq"].long(),
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time_seq=batch_dev["time_seq"].float(),
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sex=batch_dev["sex"].long(),
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padding_mask=batch_dev["padding_mask"].bool(),
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other_type=batch_dev["other_type"].long(),
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other_value=batch_dev["other_value"].float(),
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other_value_kind=batch_dev["other_value_kind"].long(),
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other_time=batch_dev["other_time"].float(),
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target_mode="next_token",
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)
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readout = build_readout(readout_name, reduce=readout_reduce)
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readout_out = readout(
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hidden=hidden,
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time_seq=batch_dev["time_seq"].float(),
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padding_mask=batch_dev["padding_mask"].bool(),
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readout_mask=batch_dev["readout_mask"].bool(),
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)
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return readout_out.hidden.gather(
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1,
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batch_dev["landmark_pos"].long()[:, None, None].expand(
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-1, 1, readout_out.hidden.shape[-1]
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),
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).squeeze(1)
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def make_occurred_mask(
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event_seq: torch.Tensor,
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*,
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vocab_size: int,
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device: torch.device,
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) -> torch.Tensor:
|
|
occurred = torch.zeros(event_seq.shape[0], int(vocab_size), dtype=torch.bool, device=device)
|
|
valid = (event_seq >= 0) & (event_seq < int(vocab_size))
|
|
safe = event_seq.clamp(min=0, max=int(vocab_size) - 1).to(device)
|
|
occurred.scatter_(1, safe, valid.to(device))
|
|
return occurred
|
|
|
|
|
|
def mortality_hazard_from_risk(risk: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
|
|
return -torch.log1p(-risk.clamp(0.0, 1.0 - float(eps)))
|
|
|
|
|
|
def death_risk_for_batch(
|
|
*,
|
|
model: DeepHealth,
|
|
batch: Dict[str, torch.Tensor],
|
|
device: torch.device,
|
|
model_target_mode: str,
|
|
readout_name: str,
|
|
readout_reduce: str,
|
|
dist_mode: str,
|
|
tau: float,
|
|
) -> torch.Tensor:
|
|
hidden = infer_landmark_hidden(
|
|
model=model,
|
|
batch=batch,
|
|
device=device,
|
|
model_target_mode=model_target_mode,
|
|
readout_name=readout_name,
|
|
readout_reduce=readout_reduce,
|
|
)
|
|
logits = model.calc_risk(hidden)
|
|
rho = model.calc_weibull_rho(hidden) if dist_mode == "weibull" else None
|
|
death_rho = model.calc_death_rho(hidden) if dist_mode == "mixed" else None
|
|
probabilities = probabilities_from_logits(
|
|
logits,
|
|
tau,
|
|
dist_mode=dist_mode,
|
|
rho=rho,
|
|
death_rho=death_rho,
|
|
)
|
|
return death_risk_from_probabilities(probabilities)
|
|
|
|
|
|
def historical_counts_by_group(
|
|
tokens: np.ndarray,
|
|
*,
|
|
death_idx: int,
|
|
token_to_group: dict[int, str],
|
|
group_names: Sequence[str],
|
|
) -> tuple[int, dict[str, int]]:
|
|
unique_tokens = {
|
|
int(token)
|
|
for token in np.asarray(tokens, dtype=np.int64).tolist()
|
|
if int(token) not in SPECIAL_TOKENS and int(token) != int(death_idx)
|
|
}
|
|
total = len(unique_tokens)
|
|
out = {group: 0 for group in group_names}
|
|
for token in unique_tokens:
|
|
group = token_to_group.get(token)
|
|
if group in out:
|
|
out[group] += 1
|
|
return total, out
|