diff --git a/evaluate_event_free_survival.py b/evaluate_event_free_survival.py new file mode 100644 index 0000000..ef1e7ea --- /dev/null +++ b/evaluate_event_free_survival.py @@ -0,0 +1,814 @@ +"""Compute landmark future death and incident system-disease risks. + +For each selected patient and landmark age, this script computes: + +* future death risk within tau years; +* future incident disease risk for each ICD-10 chapter-derived system; +* model attribution of each historical organ/system disease set to predicted + mortality risk, computed by deleting that system's historical disease tokens + and re-querying the model; +* historical modeled-disease count; +* historical modeled-disease count within each ICD-10 chapter-derived system. + +Death is always token vocab_size - 1. Disease groups are read from +icd10_chapter_organ_mapping.csv. +""" +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence + +import numpy as np +import pandas as pd +import torch +from torch.nn.utils.rnn import pad_sequence +from torch.utils.data import DataLoader, Dataset +from tqdm.auto import tqdm + +from dataset import HealthDataset +from eval_data import load_sequence_eval_dataset +from evaluate_auc_v2 import ( + LandmarkDataset, + build_model_from_dataset, + cfg_get, + load_checkpoint_state_dict, + load_json_config, + load_model_state, + make_eval_indices, + resolve_dist_mode_for_checkpoint, + resolve_eval_device, + validate_dataset_metadata, +) +from future_risk import ( + death_risk_from_probabilities, + new_disease_risk_from_probabilities, + probabilities_from_logits, +) +from models import DeepHealth +from readouts import build_readout +from targets import CHECKUP_IDX, NO_EVENT_IDX, PAD_IDX +from train_util import load_eid_file, load_extra_info_types_file + + +SPECIAL_TOKENS = {PAD_IDX, CHECKUP_IDX, NO_EVENT_IDX} + + +def parse_int_list(value: Any) -> Optional[List[int]]: + if value is None: + return None + if isinstance(value, (list, tuple, np.ndarray)): + return [int(x) for x in value] + text = str(value).strip() + if text == "": + return None + if text.startswith("["): + values = json.loads(text) + if not isinstance(values, list): + raise ValueError( + f"Expected a JSON list, got {type(values).__name__}") + return [int(x) for x in values] + return [int(x.strip()) for x in text.split(",") if x.strip()] + + +def load_extra_info_types(value: Any) -> Optional[List[int]]: + if value is None: + return None + text = str(value) + path = Path(text) + if path.exists(): + return load_extra_info_types_file(text) + return parse_int_list(value) + + +def make_landmark_ages(start: float, stop: float, step: float) -> np.ndarray: + if step <= 0: + raise ValueError("landmark_step must be positive") + if stop < start: + raise ValueError("landmark_stop must be >= landmark_start") + # Include stop when it lands on the grid, e.g. 40,45,...,80. + return np.arange(start, stop + step * 0.5, step, dtype=np.float32) + + +def build_first_occurrence_maps_for_landmarks( + dataset: HealthDataset, + subset_indices: np.ndarray, +) -> Dict[int, tuple[np.ndarray, np.ndarray]]: + first_lists: Dict[int, list[tuple[int, float]]] = {} + for patient_id, dataset_index in enumerate(np.asarray(subset_indices, dtype=np.int64).tolist()): + s = dataset.samples[int(dataset_index)] + seq_event = np.asarray(s["event_seq"], dtype=np.int64) + seq_time = np.asarray(s["time_seq"], dtype=np.float32) + tgt_event = np.asarray(s["target_event_seq"], dtype=np.int64) + tgt_time = np.asarray(s["target_time_seq"], dtype=np.float32) + if seq_event.size == 0 or tgt_event.size == 0: + continue + + full_event = np.concatenate([seq_event, tgt_event[-1:]]) + full_time = np.concatenate([seq_time, tgt_time[-1:]]) + uniq_tokens, first_idx = np.unique(full_event, return_index=True) + for token, idx in zip(uniq_tokens.tolist(), first_idx.tolist()): + token = int(token) + if token in SPECIAL_TOKENS: + continue + first_lists.setdefault(token, []).append( + (patient_id, float(full_time[int(idx)]))) + + return { + int(token): ( + np.asarray([p for p, _ in pairs], dtype=np.int32), + np.asarray([t for _, t in pairs], dtype=np.float32), + ) + for token, pairs in first_lists.items() + if pairs + } + + +def normalize_eval_split(args: argparse.Namespace, cfg: Dict[str, Any]) -> str: + eval_split = str(cfg_get(args, cfg, "eval_split", "test")).lower() + if eval_split in {"valid", "validation"}: + return "val" + if eval_split not in {"train", "val", "test", "all"}: + raise ValueError(f"Unsupported eval_split={eval_split!r}") + return eval_split + + +def load_eval_sequence_dataset( + args: argparse.Namespace, + cfg: Dict[str, Any], +) -> tuple[Any, np.ndarray, str, str]: + eval_split = normalize_eval_split(args, cfg) + model_target_mode = str(cfg.get("model_target_mode", "next_token")).lower() + data_prefix = str(cfg.get("data_prefix", "ukb")) + labels_file = str(cfg.get("labels_file", "labels.csv")) + no_event_interval_years = float(cfg.get("no_event_interval_years", 5.0)) + include_no_event_in_uts_target = bool( + cfg.get("include_no_event_in_uts_target", False)) + extra_info_types = load_extra_info_types(args.extra_info_types) + if extra_info_types is None: + extra_info_types = parse_int_list(cfg.get("extra_info_types", None)) + + print("Loading one sequence eval dataset...") + dataset = load_sequence_eval_dataset( + model_target_mode=model_target_mode, + data_prefix=data_prefix, + labels_file=labels_file, + no_event_interval_years=no_event_interval_years, + include_no_event_in_uts_target=include_no_event_in_uts_target, + min_history_events=int(cfg.get("all_future_min_history_events", 1)), + min_future_events=int(cfg.get("all_future_min_future_events", 1)), + extra_info_types=extra_info_types, + ) + + train_eid_file = cfg_get(args, cfg, "train_eid_file", "ukb_train_eid.csv") + val_eid_file = cfg_get(args, cfg, "val_eid_file", "ukb_val_eid.csv") + test_eid_file = cfg_get(args, cfg, "test_eid_file", "ukb_test_eid.csv") + split_files_exist = all( + Path(str(path)).exists() + for path in (train_eid_file, val_eid_file, test_eid_file) + ) + + if eval_split != "all" and split_files_exist: + split_files = { + "train": train_eid_file, + "val": val_eid_file, + "test": test_eid_file, + } + selected_eids = load_eid_file(split_files[eval_split]) + out = np.asarray( + [ + idx + for idx, sample in enumerate(dataset.samples) + if int(sample["eid"]) in selected_eids + ], + dtype=np.int64, + ) + if out.size == 0: + raise ValueError( + f"No samples found for eval_split={eval_split!r} using {split_files[eval_split]}" + ) + split_source = "eid_files" + else: + if eval_split == "all": + out = np.arange(len(dataset.samples), dtype=np.int64) + split_source = "all" + else: + out = make_eval_indices(dataset, args, cfg) + split_source = "ratio_split" + + subset_size = cfg_get(args, cfg, "dataset_subset_size", None) + if subset_size is not None and int(subset_size) > 0: + out = out[: int(subset_size)] + return dataset, np.asarray(out, dtype=np.int64), eval_split, split_source + + +def load_organ_groups( + path: Path, + *, + vocab_size: int, +) -> tuple[dict[str, list[int]], dict[str, str], dict[int, str]]: + table = pd.read_csv(path) + required = {"token_id", "organ_system", "organ_system_label", "is_death"} + missing = required - set(table.columns) + if missing: + raise ValueError(f"{path} is missing columns: {sorted(missing)}") + + death_idx = int(vocab_size) - 1 + groups: dict[str, list[int]] = {} + labels: dict[str, str] = {} + token_to_group: dict[int, str] = {} + for row in table.itertuples(index=False): + token = int(getattr(row, "token_id")) + if token in SPECIAL_TOKENS or token == death_idx: + continue + if token < 0 or token >= int(vocab_size): + continue + if int(getattr(row, "is_death")) == 1: + continue + group = str(getattr(row, "organ_system")) + label = str(getattr(row, "organ_system_label")) + groups.setdefault(group, []).append(token) + labels[group] = label + token_to_group[token] = group + + groups = {k: sorted(set(v)) for k, v in groups.items() if v} + return groups, labels, token_to_group + + +class IndexedLandmarkDataset(Dataset): + def __init__(self, base: LandmarkDataset) -> None: + self.base = base + + def __len__(self) -> int: + return len(self.base) + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + item = dict(self.base[idx]) + item["row_idx"] = torch.tensor(int(idx), dtype=torch.long) + return item + + +def collate_indexed_landmark_fn(batch: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]: + event_seq = pad_sequence( + [x["event_seq"] for x in batch], batch_first=True, padding_value=PAD_IDX + ) + time_seq = pad_sequence( + [x["time_seq"] for x in batch], batch_first=True, padding_value=0.0 + ) + readout_mask = pad_sequence( + [x["readout_mask"] for x in batch], batch_first=True, padding_value=False + ) + other_type = pad_sequence( + [x["other_type"] for x in batch], batch_first=True, padding_value=0 + ) + other_value = pad_sequence( + [x["other_value"] for x in batch], batch_first=True, padding_value=0.0 + ) + other_value_kind = pad_sequence( + [x["other_value_kind"] for x in batch], batch_first=True, padding_value=0 + ) + other_time = pad_sequence( + [x["other_time"] for x in batch], batch_first=True, padding_value=0.0 + ) + return { + "event_seq": event_seq, + "time_seq": time_seq, + "padding_mask": event_seq > PAD_IDX, + "readout_mask": readout_mask, + "sex": torch.stack([x["sex"] for x in batch]), + "other_type": other_type, + "other_value": other_value, + "other_value_kind": other_value_kind, + "other_time": other_time, + "landmark_pos": torch.stack([x["landmark_pos"] for x in batch]), + "t_query": torch.stack([x["t_query"] for x in batch]), + "patient_id": torch.stack([x["patient_id"] for x in batch]), + "landmark_age": torch.stack([x["landmark_age"] for x in batch]), + "followup_end_time": torch.stack([x["followup_end_time"] for x in batch]), + "death_time": torch.stack([x["death_time"] for x in batch]), + "row_idx": torch.stack([x["row_idx"] for x in batch]), + } + + +def build_group_ablated_slice( + batch: Dict[str, torch.Tensor], + token_ids: Sequence[int], + row_indices: torch.Tensor, +) -> Dict[str, torch.Tensor]: + """Build one fixed-width ablated slice without rebuilding variable-length rows.""" + event_seq = batch["event_seq"] + + out: Dict[str, torch.Tensor] = {} + out["event_seq"] = event_seq[row_indices].clone() + out["time_seq"] = batch["time_seq"][row_indices] + out["readout_mask"] = batch["readout_mask"][row_indices].clone() + out["padding_mask"] = batch["padding_mask"][row_indices].bool().clone() + out["landmark_pos"] = batch["landmark_pos"][row_indices].clone() + + seq_len = int(event_seq.shape[1]) + positions = torch.arange(seq_len, device=event_seq.device)[None, :] + ids = torch.as_tensor(token_ids, dtype=event_seq.dtype, + device=event_seq.device) + remove = torch.isin(out["event_seq"], ids) & out["padding_mask"] + out["event_seq"] = torch.where( + remove, + torch.full_like(out["event_seq"], PAD_IDX), + out["event_seq"], + ) + out["padding_mask"] &= ~remove + out["readout_mask"] &= ~remove + + has_valid = out["padding_mask"].any(dim=1) + if not bool(has_valid.all().item()): + empty_rows = torch.nonzero(~has_valid, as_tuple=False).flatten() + out["event_seq"][empty_rows, 0] = CHECKUP_IDX + out["time_seq"][empty_rows, 0] = batch["t_query"][row_indices[empty_rows]].to( + dtype=out["time_seq"].dtype + ) + out["padding_mask"][empty_rows, 0] = True + out["readout_mask"][empty_rows, 0] = True + out["landmark_pos"][empty_rows] = 0 + + has_readout = out["readout_mask"].any(dim=1) + if not bool(has_readout.all().item()): + rows = torch.nonzero(~has_readout, as_tuple=False).flatten() + local_valid = out["padding_mask"][rows] + last_pos = torch.where( + local_valid, + positions.expand(local_valid.shape[0], -1), + torch.zeros_like(positions.expand(local_valid.shape[0], -1)), + ).amax(dim=1) + out["readout_mask"][rows] = False + out["readout_mask"][rows, last_pos] = True + out["landmark_pos"][rows] = last_pos.to(dtype=out["landmark_pos"].dtype) + + repeated_keys = ( + "sex", + "other_type", + "other_value", + "other_value_kind", + "other_time", + "t_query", + "patient_id", + "landmark_age", + "followup_end_time", + "death_time", + "row_idx", + ) + for key in repeated_keys: + out[key] = batch[key][row_indices] + return out + + +def concat_tensor_batches(chunks: Sequence[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]: + return { + key: torch.cat([chunk[key] for chunk in chunks], dim=0) + for key in chunks[0] + } + + +def iter_group_ablated_batches( + batch: Dict[str, torch.Tensor], + group_names: Sequence[str], + organ_groups: dict[str, list[int]], + occurred: torch.Tensor, + max_batch_size: int, +): + """Yield ablated chunks as soon as enough rows are available for a forward pass.""" + pending_batches: list[Dict[str, torch.Tensor]] = [] + pending_groups: list[str] = [] + pending_rows: list[int] = [] + pending_n = 0 + + for group in group_names: + ids = torch.as_tensor( + organ_groups[group], dtype=torch.long, device=occurred.device) + if ids.numel() == 0: + continue + active_rows = torch.nonzero( + occurred[:, ids].any(dim=1), as_tuple=False).flatten() + if active_rows.numel() == 0: + continue + + row_offset = 0 + while row_offset < int(active_rows.numel()): + capacity = int(max_batch_size) - pending_n + row_stop = min(int(active_rows.numel()), row_offset + capacity) + row_indices = active_rows[row_offset:row_stop].to( + device=batch["event_seq"].device) + chunk = build_group_ablated_slice( + batch=batch, + token_ids=organ_groups[group], + row_indices=row_indices, + ) + chunk_n = int(row_indices.numel()) + pending_batches.append(chunk) + pending_groups.extend([group] * chunk_n) + pending_rows.extend(int(x) + for x in row_indices.detach().cpu().tolist()) + pending_n += chunk_n + row_offset = row_stop + + if pending_n >= int(max_batch_size): + yield concat_tensor_batches(pending_batches), pending_groups, pending_rows + pending_batches = [] + pending_groups = [] + pending_rows = [] + pending_n = 0 + + if pending_batches: + yield concat_tensor_batches(pending_batches), pending_groups, pending_rows + + +@torch.no_grad() +def infer_landmark_hidden( + *, + model: DeepHealth, + batch: Dict[str, torch.Tensor], + device: torch.device, + model_target_mode: str, + readout_name: str, + readout_reduce: str, +) -> torch.Tensor: + batch_dev = { + k: (v.to(device, non_blocking=True) if isinstance(v, torch.Tensor) else v) + for k, v in batch.items() + } + if model_target_mode == "all_future": + return model( + event_seq=batch_dev["event_seq"].long(), + time_seq=batch_dev["time_seq"].float(), + sex=batch_dev["sex"].long(), + padding_mask=batch_dev["padding_mask"].bool(), + t_query=batch_dev["t_query"].float(), + other_type=batch_dev["other_type"].long(), + other_value=batch_dev["other_value"].float(), + other_value_kind=batch_dev["other_value_kind"].long(), + other_time=batch_dev["other_time"].float(), + target_mode="all_future", + ) + + hidden = model( + event_seq=batch_dev["event_seq"].long(), + time_seq=batch_dev["time_seq"].float(), + sex=batch_dev["sex"].long(), + padding_mask=batch_dev["padding_mask"].bool(), + other_type=batch_dev["other_type"].long(), + other_value=batch_dev["other_value"].float(), + other_value_kind=batch_dev["other_value_kind"].long(), + other_time=batch_dev["other_time"].float(), + target_mode="next_token", + ) + readout = build_readout(readout_name, reduce=readout_reduce) + readout_out = readout( + hidden=hidden, + time_seq=batch_dev["time_seq"].float(), + padding_mask=batch_dev["padding_mask"].bool(), + readout_mask=batch_dev["readout_mask"].bool(), + ) + return readout_out.hidden.gather( + 1, + batch_dev["landmark_pos"].long()[:, None, None].expand( + -1, 1, readout_out.hidden.shape[-1] + ), + ).squeeze(1) + + +def make_occurred_mask( + event_seq: torch.Tensor, + *, + vocab_size: int, + device: torch.device, +) -> 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 + + +def output_name_for_run(run_path: Path, eval_split: str, tau: float) -> Path: + return run_path / f"future_risk_{eval_split}_tau{tau:g}y.csv" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Compute landmark death and incident system-disease risks." + ) + parser.add_argument("--run_path", type=str, required=True) + parser.add_argument("--output_path", type=str, default=None) + parser.add_argument("--organ_mapping_path", type=str, + default="icd10_chapter_organ_mapping.csv") + parser.add_argument("--eval_split", type=str, default=None) + parser.add_argument("--dataset_subset_size", type=int, default=None) + parser.add_argument("--train_eid_file", type=str, default=None) + parser.add_argument("--val_eid_file", type=str, default=None) + parser.add_argument("--test_eid_file", type=str, default=None) + parser.add_argument("--landmark_start", type=float, default=40.0) + parser.add_argument("--landmark_stop", type=float, default=80.0) + parser.add_argument("--landmark_step", type=float, default=5.0) + parser.add_argument("--tau", type=float, default=5.0) + parser.add_argument("--min_history_events", type=int, default=None) + parser.add_argument("--batch_size", type=int, default=None) + parser.add_argument( + "--attribution_batch_size", + type=int, + default=None, + help="Forward batch size for expanded organ/system ablation queries.", + ) + parser.add_argument("--num_workers", type=int, default=None) + parser.add_argument("--device", type=str, default=None) + parser.add_argument("--extra_info_types", type=str, default=None) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + run_path = Path(args.run_path) + config_path = run_path / "train_config.json" + checkpoint_path = run_path / "best_model.pt" + if not config_path.exists(): + raise FileNotFoundError(f"train_config.json not found: {config_path}") + if not checkpoint_path.exists(): + raise FileNotFoundError(f"best_model.pt not found: {checkpoint_path}") + + cfg = load_json_config(config_path) + model_target_mode = str(cfg.get("model_target_mode", "next_token")).lower() + if model_target_mode not in {"next_token", "all_future"}: + raise ValueError( + f"Unsupported model_target_mode: {model_target_mode!r}") + + target_mode = str(cfg.get("target_mode", "uts")) + attn_mask_mode = str( + cfg.get("attn_mask_mode", "non_strict_time" if target_mode == + "uts" else "target_aware") + ) + readout_name = str(cfg.get( + "readout_name", "same_time_group_end" if target_mode == "uts" else "token")) + readout_reduce = str(cfg.get("readout_reduce", "mean")) + + dataset, subset_indices, eval_split, split_source = load_eval_sequence_dataset( + args, + cfg, + ) + validate_dataset_metadata(dataset, cfg) + + landmark_ages = make_landmark_ages( + float(args.landmark_start), + float(args.landmark_stop), + float(args.landmark_step), + ) + tau = float(args.tau) + if tau < 0: + raise ValueError("tau must be non-negative") + + first_occurrence_by_token = build_first_occurrence_maps_for_landmarks( + dataset, + subset_indices, + ) + death_idx = int(dataset.vocab_size) - 1 + landmark_dataset = LandmarkDataset( + dataset=dataset, + subset_indices=subset_indices, + landmark_ages=landmark_ages, + attn_mask_mode=attn_mask_mode, + model_target_mode=model_target_mode, + min_history_events=int(cfg_get(args, cfg, "min_history_events", 1)), + first_occurrence_by_token=first_occurrence_by_token, + death_token_ids=[death_idx], + ) + + organ_groups, organ_labels, token_to_group = load_organ_groups( + Path(args.organ_mapping_path), + vocab_size=int(dataset.vocab_size), + ) + group_names = sorted(organ_groups) + + state_dict = load_checkpoint_state_dict(checkpoint_path, map_location="cpu") + dist_mode = resolve_dist_mode_for_checkpoint( + str(cfg.get("dist_mode", "exponential")), state_dict) + cfg_model = dict(cfg) + cfg_model["dist_mode"] = dist_mode + device = resolve_eval_device(args.device) + model = build_model_from_dataset(args, cfg_model, dataset).to(device) + load_model_state(model, state_dict) + model.eval() + + batch_size = int(cfg_get(args, cfg, "batch_size", 128)) + attribution_batch_size = int( + cfg_get(args, cfg, "attribution_batch_size", + max(batch_size * 4, batch_size)) + ) + if attribution_batch_size <= 0: + raise ValueError("attribution_batch_size must be positive") + num_workers = int(cfg_get(args, cfg, "num_workers", 4)) + loader = DataLoader( + IndexedLandmarkDataset(landmark_dataset), + batch_size=batch_size, + shuffle=False, + collate_fn=collate_indexed_landmark_fn, + num_workers=num_workers, + pin_memory=device.type == "cuda", + persistent_workers=num_workers > 0, + prefetch_factor=2 if num_workers > 0 else None, + ) + + output_path = Path(args.output_path) if args.output_path else output_name_for_run( + run_path, eval_split, tau) + output_path.parent.mkdir(parents=True, exist_ok=True) + + print(f"Eval split: {eval_split}") + print(f"Split source: {split_source}") + print(f"Selected patients: {len(subset_indices)}") + print(f"Landmark ages: {landmark_ages.tolist()}") + print(f"Tau: {tau:g} years") + print(f"Dist mode: {dist_mode}") + print(f"Device: {device}") + print(f"Death token: {death_idx}") + print(f"Organ/system groups: {len(group_names)}") + print(f"Landmark rows: {len(landmark_dataset)}") + print(f"Attribution batch size: {attribution_batch_size}") + print(f"Output: {output_path}") + + rows: list[dict[str, Any]] = [] + for batch in tqdm(loader, desc="Future risks", dynamic_ncols=True): + 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, + ) + occurred = make_occurred_mask( + batch["event_seq"].to(device), + vocab_size=int(dataset.vocab_size), + device=device, + ) + + death_risk_tensor = death_risk_from_probabilities(probabilities) + death_hazard_tensor = mortality_hazard_from_risk(death_risk_tensor) + death_risk = death_risk_tensor.detach().cpu().numpy() + + group_risk: dict[str, np.ndarray] = {} + for group in group_names: + group_risk[group] = new_disease_risk_from_probabilities( + probabilities, + occurred, + organ_groups[group], + ).detach().cpu().numpy() + + group_mortality_attr_prob: dict[str, np.ndarray] = {} + group_mortality_attr_hazard: dict[str, np.ndarray] = {} + batch_n = int(batch["event_seq"].shape[0]) + zeros = np.zeros(batch_n, dtype=np.float32) + for group in group_names: + group_mortality_attr_prob[group] = zeros.copy() + group_mortality_attr_hazard[group] = zeros.copy() + + for ablated_chunk, chunk_groups, chunk_rows in iter_group_ablated_batches( + batch=batch, + group_names=group_names, + organ_groups=organ_groups, + occurred=occurred, + max_batch_size=attribution_batch_size, + ): + ablated_death_risk = death_risk_for_batch( + model=model, + batch=ablated_chunk, + device=device, + model_target_mode=model_target_mode, + readout_name=readout_name, + readout_reduce=readout_reduce, + dist_mode=dist_mode, + tau=tau, + ) + row_tensor = torch.as_tensor( + chunk_rows, dtype=torch.long, device=device) + ablated_death_hazard = mortality_hazard_from_risk( + ablated_death_risk) + attr_prob = ( + death_risk_tensor[row_tensor] - ablated_death_risk + ).detach().cpu().numpy() + attr_hazard = ( + death_hazard_tensor[row_tensor] - ablated_death_hazard + ).detach().cpu().numpy() + for local_idx, (group, row_idx) in enumerate(zip(chunk_groups, chunk_rows)): + group_mortality_attr_prob[group][row_idx] = attr_prob[local_idx] + group_mortality_attr_hazard[group][row_idx] = attr_hazard[local_idx] + + row_indices = batch["row_idx"].cpu().numpy().astype(np.int64) + for j, row_idx in enumerate(row_indices.tolist()): + meta = landmark_dataset.rows[int(row_idx)] + dataset_index = int(meta["dataset_index"]) + sample = dataset.samples[dataset_index] + hist_tokens = np.asarray(meta["event_seq"], dtype=np.int64) + total_count, group_counts = historical_counts_by_group( + hist_tokens, + death_idx=death_idx, + token_to_group=token_to_group, + group_names=group_names, + ) + + out: dict[str, Any] = { + "patient_id": int(meta["patient_id"]), + "dataset_index": dataset_index, + "eid": int(sample.get("eid", -1)), + "sex": int(meta["sex"]), + "landmark_age": float(meta["landmark_age"]), + "tau": tau, + "followup_end_time": float(meta["followup_end_time"]), + "history_disease_count": int(total_count), + "death_risk": float(death_risk[j]), + } + for group in group_names: + out[f"history_count__{group}"] = int(group_counts[group]) + out[f"new_disease_risk__{group}"] = float(group_risk[group][j]) + if int(group_counts[group]) == 0: + group_mortality_attr_prob[group][j] = 0.0 + group_mortality_attr_hazard[group][j] = 0.0 + out[f"mortality_attribution_probability__{group}"] = float( + group_mortality_attr_prob[group][j] + ) + out[f"mortality_attribution_hazard__{group}"] = float( + group_mortality_attr_hazard[group][j] + ) + rows.append(out) + + df = pd.DataFrame(rows) + df.to_csv(output_path, index=False) + print(f"Wrote {len(df)} rows to {output_path}") + + +if __name__ == "__main__": + main()