706 lines
26 KiB
Python
706 lines
26 KiB
Python
"""Compute per-disease attribution to predicted mortality risk.
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For each selected patient and landmark age, this script keeps only rows where
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each scanned disease token has already occurred in the history. It then deletes
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that historical disease token, re-queries the model, and reports both
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differences and ratios on probability and cumulative-hazard scales. If
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--disease is omitted, all disease tokens in the mapping are scanned.
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Death is always token vocab_size - 1.
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"""
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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
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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.utils.data import DataLoader
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from tqdm.auto import tqdm
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from evaluate_auc_v2 import (
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build_model_from_dataset,
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cfg_get,
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load_checkpoint_state_dict,
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load_json_config,
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load_model_state,
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resolve_dist_mode_for_checkpoint,
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resolve_eval_device,
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validate_dataset_metadata,
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)
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from evaluate_event_free_survival import (
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IndexedLandmarkDataset,
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LandmarkDataset,
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build_first_occurrence_maps_for_landmarks,
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build_group_ablated_slice,
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collate_indexed_landmark_fn,
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death_risk_for_batch,
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historical_counts_by_group,
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infer_landmark_hidden,
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load_eval_sequence_dataset,
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load_organ_groups,
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make_landmark_ages,
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make_occurred_mask,
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mortality_hazard_from_risk,
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)
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from future_risk import death_risk_from_probabilities, probabilities_from_logits
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OUTPUT_COLUMNS = [
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"patient_id",
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"dataset_index",
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"eid",
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"sex",
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"landmark_age",
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"tau",
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"followup_end_time",
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"history_disease_count",
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"selected_disease_history_count",
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"selected_disease_token_id",
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"selected_disease_code",
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"selected_disease_name",
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"selected_disease_organ_system",
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"selected_disease_organ_system_label",
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"history_count__selected_organ_system",
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"death_risk",
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"death_hazard",
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"ablated_death_risk",
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"ablated_death_hazard",
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"mortality_attribution_probability",
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"mortality_attribution_hazard",
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"mortality_attribution_probability_ratio",
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"mortality_attribution_hazard_ratio",
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]
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SUMMARY_KEY_COLUMNS = [
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"selected_disease_token_id",
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"selected_disease_code",
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"selected_disease_name",
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"selected_disease_organ_system",
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"selected_disease_organ_system_label",
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"landmark_age",
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"sex",
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]
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SUMMARY_MEAN_COLUMNS = [
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"history_disease_count",
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"selected_disease_history_count",
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"history_count__selected_organ_system",
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"death_risk",
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"death_hazard",
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"ablated_death_risk",
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"ablated_death_hazard",
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"mortality_attribution_probability",
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"mortality_attribution_hazard",
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"mortality_attribution_probability_ratio",
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"mortality_attribution_hazard_ratio",
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]
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def write_compressed_npz_table(path: Path, table: pd.DataFrame) -> int:
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table = table.reindex(columns=OUTPUT_COLUMNS)
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arrays: dict[str, np.ndarray] = {
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"__columns__": np.asarray(OUTPUT_COLUMNS, dtype="U"),
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}
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for column in OUTPUT_COLUMNS:
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values = table[column] if column in table else pd.Series([], dtype=object)
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if values.dtype == object:
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arrays[column] = values.fillna("").astype(str).to_numpy(dtype="U")
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else:
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arrays[column] = values.to_numpy()
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np.savez_compressed(path, **arrays)
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return int(len(table))
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def normalize_output_dir(path: Path) -> Path:
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if path.suffix:
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return path.with_suffix(path.suffix + "_shards")
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return path
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def write_manifest(
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output_dir: Path,
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*,
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rows: int,
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shards: list[dict[str, Any]],
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summary_file: str,
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scanned_diseases: list[dict[str, Any]],
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eval_split: str,
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tau: float,
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landmark_start: float,
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landmark_stop: float,
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landmark_step: float,
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) -> None:
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payload = {
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"format": "compressed_npz_shards",
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"columns": OUTPUT_COLUMNS,
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"rows": int(rows),
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"shards": shards,
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"summary_file": summary_file,
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"scanned_diseases": scanned_diseases,
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"eval_split": eval_split,
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"tau": float(tau),
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"landmark_start": float(landmark_start),
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"landmark_stop": float(landmark_stop),
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"landmark_step": float(landmark_step),
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}
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with (output_dir / "manifest.json").open("w", encoding="utf-8") as f:
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json.dump(payload, f, ensure_ascii=False, indent=2)
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def update_summary_accumulator(
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summary: dict[tuple[Any, ...], dict[str, float]],
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table: pd.DataFrame,
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) -> None:
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if table.empty:
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return
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grouped = table.groupby(SUMMARY_KEY_COLUMNS, dropna=False, sort=False)
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for key, group in grouped:
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if not isinstance(key, tuple):
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key = (key,)
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acc = summary.setdefault(
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key,
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{"n": 0.0, **{column: 0.0 for column in SUMMARY_MEAN_COLUMNS}},
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)
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n = int(len(group))
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acc["n"] += float(n)
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for column in SUMMARY_MEAN_COLUMNS:
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acc[column] += float(pd.to_numeric(group[column], errors="coerce").sum())
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def write_summary_csv(
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path: Path,
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summary: dict[tuple[Any, ...], dict[str, float]],
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) -> int:
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rows: list[dict[str, Any]] = []
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for key, acc in summary.items():
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n = int(acc["n"])
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out = {column: value for column, value in zip(SUMMARY_KEY_COLUMNS, key)}
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out["n"] = n
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for column in SUMMARY_MEAN_COLUMNS:
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out[f"mean__{column}"] = acc[column] / n if n > 0 else np.nan
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rows.append(out)
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columns = [
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*SUMMARY_KEY_COLUMNS,
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"n",
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*[f"mean__{column}" for column in SUMMARY_MEAN_COLUMNS],
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]
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pd.DataFrame(rows, columns=columns).sort_values(
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["selected_disease_token_id", "landmark_age", "sex"],
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kind="mergesort",
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).to_csv(path, index=False)
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return len(rows)
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def load_disease_metadata(
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mapping_path: Path,
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*,
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vocab_size: int,
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) -> dict[int, dict[str, Any]]:
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if not mapping_path.exists():
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raise FileNotFoundError(f"Disease mapping file not found: {mapping_path}")
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table = pd.read_csv(mapping_path)
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required = {"token_id", "code", "name", "is_death"}
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missing = required - set(table.columns)
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if missing:
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raise ValueError(f"{mapping_path} is missing columns: {sorted(missing)}")
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death_idx = int(vocab_size) - 1
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out: dict[int, dict[str, Any]] = {}
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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 < 0 or token >= int(vocab_size) or token == death_idx:
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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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meta = {
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"token_id": token,
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"code": str(getattr(row, "code")),
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"name": str(getattr(row, "name")),
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}
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for column in (
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"icd10_chapter",
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"icd10_chapter_title",
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"organ_system",
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"organ_system_label",
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):
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if hasattr(row, column):
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meta[column] = str(getattr(row, column))
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out[token] = meta
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return out
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def resolve_disease_token(
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value: str,
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metadata: dict[int, dict[str, Any]],
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) -> tuple[int, dict[str, Any]]:
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text = str(value).strip()
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if text == "":
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raise ValueError("--disease must not be empty")
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if text.isdigit() or (text.startswith("-") and text[1:].isdigit()):
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token = int(text)
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if token not in metadata:
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raise ValueError(f"Disease token_id {token} was not found in the mapping")
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return token, metadata[token]
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lower = text.lower()
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exact = [
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(token, meta)
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for token, meta in metadata.items()
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if str(meta.get("code", "")).lower() == lower
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or str(meta.get("name", "")).lower() == lower
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]
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if len(exact) == 1:
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return exact[0]
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if len(exact) > 1:
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raise ValueError(f"--disease={value!r} matched multiple diseases exactly")
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contains = [
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(token, meta)
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for token, meta in metadata.items()
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if lower in str(meta.get("code", "")).lower()
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or lower in str(meta.get("name", "")).lower()
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]
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if len(contains) == 1:
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return contains[0]
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if not contains:
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raise ValueError(f"--disease={value!r} did not match any disease token")
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preview = ", ".join(
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f"{token}:{meta.get('code')} ({meta.get('name')})"
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for token, meta in contains[:10]
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)
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raise ValueError(
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f"--disease={value!r} matched {len(contains)} diseases; use token_id or code. "
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f"First matches: {preview}"
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)
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def resolve_disease_tokens(
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value: str | None,
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metadata: dict[int, dict[str, Any]],
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) -> list[tuple[int, dict[str, Any]]]:
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if value is None or str(value).strip() == "":
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return [(token, metadata[token]) for token in sorted(metadata)]
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out: list[tuple[int, dict[str, Any]]] = []
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seen: set[int] = set()
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for part in str(value).split(","):
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token, meta = resolve_disease_token(part, metadata)
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if token not in seen:
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out.append((token, meta))
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seen.add(token)
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return out
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def safe_ratio(
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numerator: torch.Tensor,
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denominator: torch.Tensor,
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*,
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eps: float,
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) -> torch.Tensor:
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return numerator / denominator.clamp_min(float(eps))
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def output_name_for_run(run_path: Path, eval_split: str, tau: float, *, all_diseases: bool) -> Path:
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scope = "all_diseases" if all_diseases else "selected_diseases"
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return run_path / f"single_disease_mortality_attribution_{eval_split}_{scope}_tau{tau:g}y"
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(
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description="Compute per-disease model attribution to mortality risk."
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)
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parser.add_argument("--run_path", type=str, required=True)
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parser.add_argument(
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"--disease",
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type=str,
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default=None,
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help=(
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"Optional disease token_id, ICD-10 code, exact name, unambiguous name "
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"substring, or comma-separated list. If omitted, scan all disease tokens."
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),
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)
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parser.add_argument(
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"--output_path",
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type=str,
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default=None,
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help="Output directory for compressed .npz shards.",
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)
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parser.add_argument("--organ_mapping_path", type=str, default="icd10_chapter_organ_mapping.csv")
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parser.add_argument("--eval_split", type=str, default=None)
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parser.add_argument("--dataset_subset_size", type=int, default=None)
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parser.add_argument("--train_eid_file", type=str, default=None)
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parser.add_argument("--val_eid_file", type=str, default=None)
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parser.add_argument("--test_eid_file", type=str, default=None)
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parser.add_argument("--landmark_start", type=float, default=40.0)
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parser.add_argument("--landmark_stop", type=float, default=80.0)
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parser.add_argument("--landmark_step", type=float, default=5.0)
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parser.add_argument("--tau", type=float, default=5.0)
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parser.add_argument("--min_history_events", type=int, default=None)
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parser.add_argument("--batch_size", type=int, default=None)
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parser.add_argument(
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"--attribution_batch_size",
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type=int,
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default=None,
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help="Forward batch size for disease-token ablation queries.",
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)
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parser.add_argument("--num_workers", type=int, default=None)
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parser.add_argument("--device", type=str, default=None)
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parser.add_argument("--extra_info_types", type=str, default=None)
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parser.add_argument(
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"--ratio_eps",
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type=float,
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default=1e-7,
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help="Small lower bound for ratio denominators.",
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)
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return parser.parse_args()
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def main() -> None:
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args = parse_args()
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run_path = Path(args.run_path)
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config_path = run_path / "train_config.json"
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checkpoint_path = run_path / "best_model.pt"
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if not config_path.exists():
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raise FileNotFoundError(f"train_config.json not found: {config_path}")
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if not checkpoint_path.exists():
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raise FileNotFoundError(f"best_model.pt not found: {checkpoint_path}")
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cfg = load_json_config(config_path)
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model_target_mode = str(cfg.get("model_target_mode", "next_token")).lower()
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if model_target_mode not in {"next_token", "all_future"}:
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raise ValueError(f"Unsupported model_target_mode: {model_target_mode!r}")
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target_mode = str(cfg.get("target_mode", "uts"))
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attn_mask_mode = str(
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cfg.get("attn_mask_mode", "non_strict_time" if target_mode == "uts" else "target_aware")
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)
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readout_name = str(
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cfg.get("readout_name", "same_time_group_end" if target_mode == "uts" else "token")
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)
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readout_reduce = str(cfg.get("readout_reduce", "mean"))
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dataset, subset_indices, eval_split, split_source = load_eval_sequence_dataset(args, cfg)
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validate_dataset_metadata(dataset, cfg)
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metadata = load_disease_metadata(
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Path(args.organ_mapping_path),
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vocab_size=int(dataset.vocab_size),
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)
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scanned_disease_items = resolve_disease_tokens(args.disease, metadata)
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if not scanned_disease_items:
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raise ValueError("No diseases selected for attribution")
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scanned_disease_tokens = [token for token, _meta in scanned_disease_items]
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scanned_disease_token_set = set(scanned_disease_tokens)
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landmark_ages = make_landmark_ages(
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float(args.landmark_start),
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float(args.landmark_stop),
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float(args.landmark_step),
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)
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tau = float(args.tau)
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if tau < 0:
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raise ValueError("tau must be non-negative")
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first_occurrence_by_token = build_first_occurrence_maps_for_landmarks(
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dataset,
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subset_indices,
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)
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death_idx = int(dataset.vocab_size) - 1
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landmark_dataset = LandmarkDataset(
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dataset=dataset,
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subset_indices=subset_indices,
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landmark_ages=landmark_ages,
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attn_mask_mode=attn_mask_mode,
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model_target_mode=model_target_mode,
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min_history_events=int(cfg_get(args, cfg, "min_history_events", 1)),
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first_occurrence_by_token=first_occurrence_by_token,
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death_token_ids=[death_idx],
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)
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organ_groups, _organ_labels, token_to_group = load_organ_groups(
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Path(args.organ_mapping_path),
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vocab_size=int(dataset.vocab_size),
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)
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group_names = sorted(organ_groups)
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state_dict = load_checkpoint_state_dict(checkpoint_path, map_location="cpu")
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dist_mode = resolve_dist_mode_for_checkpoint(str(cfg.get("dist_mode", "exponential")), state_dict)
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cfg_model = dict(cfg)
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cfg_model["dist_mode"] = dist_mode
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device = resolve_eval_device(args.device)
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model = build_model_from_dataset(args, cfg_model, dataset).to(device)
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load_model_state(model, state_dict)
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model.eval()
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batch_size = int(cfg_get(args, cfg, "batch_size", 128))
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attribution_batch_size = int(
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cfg_get(args, cfg, "attribution_batch_size", max(batch_size * 4, batch_size))
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)
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if attribution_batch_size <= 0:
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raise ValueError("attribution_batch_size must be positive")
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if float(args.ratio_eps) <= 0:
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raise ValueError("--ratio_eps must be positive")
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num_workers = int(cfg_get(args, cfg, "num_workers", 4))
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loader = DataLoader(
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IndexedLandmarkDataset(landmark_dataset),
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batch_size=batch_size,
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shuffle=False,
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collate_fn=collate_indexed_landmark_fn,
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num_workers=num_workers,
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pin_memory=device.type == "cuda",
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persistent_workers=num_workers > 0,
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prefetch_factor=2 if num_workers > 0 else None,
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)
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output_path = (
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Path(args.output_path)
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if args.output_path
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else output_name_for_run(
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run_path,
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eval_split,
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tau,
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all_diseases=args.disease is None or str(args.disease).strip() == "",
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)
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)
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output_dir = normalize_output_dir(output_path)
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output_dir.mkdir(parents=True, exist_ok=True)
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print(f"Eval split: {eval_split}")
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print(f"Split source: {split_source}")
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print(f"Selected patients: {len(subset_indices)}")
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print(f"Landmark ages: {landmark_ages.tolist()}")
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print(f"Tau: {tau:g} years")
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print(f"Dist mode: {dist_mode}")
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print(f"Device: {device}")
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print(f"Death token: {death_idx}")
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if len(scanned_disease_items) == len(metadata):
|
|
print(f"Diseases: all mapped diseases ({len(scanned_disease_items)})")
|
|
else:
|
|
preview = ", ".join(
|
|
f"{token}:{meta.get('code')}" for token, meta in scanned_disease_items[:10]
|
|
)
|
|
print(f"Diseases: {len(scanned_disease_items)} selected ({preview})")
|
|
print(f"Landmark rows: {len(landmark_dataset)}")
|
|
print(f"Attribution batch size: {attribution_batch_size}")
|
|
print(f"Output directory: {output_dir}")
|
|
|
|
written_rows = 0
|
|
shard_index = 0
|
|
shards: list[dict[str, Any]] = []
|
|
summary_accumulator: dict[tuple[Any, ...], dict[str, float]] = {}
|
|
pending_batch_chunks: list[Dict[str, torch.Tensor]] = []
|
|
pending_meta_chunks: list[list[dict[str, Any]]] = []
|
|
pending_n = 0
|
|
|
|
def flush_pending() -> None:
|
|
nonlocal written_rows, shard_index, pending_batch_chunks, pending_meta_chunks, pending_n
|
|
if pending_n == 0:
|
|
return
|
|
|
|
ablated_batch = {
|
|
key: torch.cat([chunk[key] for chunk in pending_batch_chunks], dim=0)
|
|
for key in pending_batch_chunks[0]
|
|
}
|
|
meta_rows = [row for chunk in pending_meta_chunks for row in chunk]
|
|
with torch.no_grad():
|
|
ablated_risk = death_risk_for_batch(
|
|
model=model,
|
|
batch=ablated_batch,
|
|
device=device,
|
|
model_target_mode=model_target_mode,
|
|
readout_name=readout_name,
|
|
readout_reduce=readout_reduce,
|
|
dist_mode=dist_mode,
|
|
tau=tau,
|
|
)
|
|
ablated_hazard = mortality_hazard_from_risk(ablated_risk)
|
|
orig_risk = torch.as_tensor(
|
|
[row.pop("_death_risk") for row in meta_rows],
|
|
dtype=ablated_risk.dtype,
|
|
device=ablated_risk.device,
|
|
)
|
|
orig_hazard = torch.as_tensor(
|
|
[row.pop("_death_hazard") for row in meta_rows],
|
|
dtype=ablated_hazard.dtype,
|
|
device=ablated_hazard.device,
|
|
)
|
|
|
|
attr_prob = orig_risk - ablated_risk
|
|
attr_hazard = orig_hazard - ablated_hazard
|
|
ratio_prob = safe_ratio(orig_risk, ablated_risk, eps=float(args.ratio_eps))
|
|
ratio_hazard = safe_ratio(orig_hazard, ablated_hazard, eps=float(args.ratio_eps))
|
|
|
|
for i, row in enumerate(meta_rows):
|
|
row["death_risk"] = float(orig_risk[i].detach().cpu())
|
|
row["death_hazard"] = float(orig_hazard[i].detach().cpu())
|
|
row["ablated_death_risk"] = float(ablated_risk[i].detach().cpu())
|
|
row["ablated_death_hazard"] = float(ablated_hazard[i].detach().cpu())
|
|
row["mortality_attribution_probability"] = float(attr_prob[i].detach().cpu())
|
|
row["mortality_attribution_hazard"] = float(attr_hazard[i].detach().cpu())
|
|
row["mortality_attribution_probability_ratio"] = float(
|
|
ratio_prob[i].detach().cpu()
|
|
)
|
|
row["mortality_attribution_hazard_ratio"] = float(
|
|
ratio_hazard[i].detach().cpu()
|
|
)
|
|
|
|
table = pd.DataFrame(meta_rows).reindex(columns=OUTPUT_COLUMNS)
|
|
update_summary_accumulator(summary_accumulator, table)
|
|
shard_name = f"part-{shard_index:06d}.npz"
|
|
shard_path = output_dir / shard_name
|
|
shard_rows = write_compressed_npz_table(shard_path, table)
|
|
shards.append({"file": shard_name, "rows": int(shard_rows)})
|
|
shard_index += 1
|
|
written_rows += len(meta_rows)
|
|
pending_batch_chunks = []
|
|
pending_meta_chunks = []
|
|
pending_n = 0
|
|
|
|
for batch in tqdm(loader, desc="Per-disease mortality attribution", 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,
|
|
)
|
|
with torch.no_grad():
|
|
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,
|
|
)
|
|
death_risk_tensor = death_risk_from_probabilities(probabilities)
|
|
death_hazard_tensor = mortality_hazard_from_risk(death_risk_tensor)
|
|
occurred = make_occurred_mask(
|
|
batch["event_seq"].to(device),
|
|
vocab_size=int(dataset.vocab_size),
|
|
device=device,
|
|
)
|
|
event_values = batch["event_seq"].detach().cpu().numpy().reshape(-1)
|
|
batch_disease_tokens = sorted(
|
|
{
|
|
int(token)
|
|
for token in event_values.tolist()
|
|
if int(token) in scanned_disease_token_set
|
|
}
|
|
)
|
|
if not batch_disease_tokens:
|
|
continue
|
|
|
|
for disease_token in batch_disease_tokens:
|
|
disease_meta = metadata[disease_token]
|
|
active_rows = torch.nonzero(
|
|
occurred[:, disease_token].to(dtype=torch.bool),
|
|
as_tuple=False,
|
|
).flatten()
|
|
if active_rows.numel() == 0:
|
|
continue
|
|
|
|
row_offset = 0
|
|
while row_offset < int(active_rows.numel()):
|
|
capacity = int(attribution_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)
|
|
ablated_chunk = build_group_ablated_slice(
|
|
batch=batch,
|
|
token_ids=[disease_token],
|
|
row_indices=row_indices,
|
|
)
|
|
|
|
meta_chunk: list[dict[str, Any]] = []
|
|
row_ids = batch["row_idx"][row_indices.cpu()].cpu().numpy().astype(np.int64)
|
|
for local_pos, row_idx in enumerate(row_ids.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,
|
|
)
|
|
disease_history_count = int((hist_tokens == int(disease_token)).sum())
|
|
if disease_history_count <= 0:
|
|
continue
|
|
|
|
orig_row = int(row_indices[local_pos].detach().cpu())
|
|
meta_chunk.append(
|
|
{
|
|
"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),
|
|
"selected_disease_history_count": disease_history_count,
|
|
"selected_disease_token_id": int(disease_token),
|
|
"selected_disease_code": str(disease_meta.get("code", "")),
|
|
"selected_disease_name": str(disease_meta.get("name", "")),
|
|
"selected_disease_organ_system": str(
|
|
disease_meta.get("organ_system", "")
|
|
),
|
|
"selected_disease_organ_system_label": str(
|
|
disease_meta.get("organ_system_label", "")
|
|
),
|
|
"history_count__selected_organ_system": int(
|
|
group_counts.get(str(disease_meta.get("organ_system", "")), 0)
|
|
),
|
|
"_death_risk": float(death_risk_tensor[orig_row].detach().cpu()),
|
|
"_death_hazard": float(death_hazard_tensor[orig_row].detach().cpu()),
|
|
}
|
|
)
|
|
|
|
if meta_chunk:
|
|
pending_batch_chunks.append(ablated_chunk)
|
|
pending_meta_chunks.append(meta_chunk)
|
|
pending_n += len(meta_chunk)
|
|
row_offset = row_stop
|
|
|
|
if pending_n >= int(attribution_batch_size):
|
|
flush_pending()
|
|
|
|
flush_pending()
|
|
if not shards:
|
|
empty_path = output_dir / "part-000000.npz"
|
|
write_compressed_npz_table(empty_path, pd.DataFrame(columns=OUTPUT_COLUMNS))
|
|
shards.append({"file": empty_path.name, "rows": 0})
|
|
summary_path = output_dir / "summary_by_disease_age_sex.csv"
|
|
summary_rows = write_summary_csv(summary_path, summary_accumulator)
|
|
write_manifest(
|
|
output_dir,
|
|
rows=written_rows,
|
|
shards=shards,
|
|
summary_file=summary_path.name,
|
|
scanned_diseases=[
|
|
{"token_id": int(token), **{k: v for k, v in meta.items() if k != "token_id"}}
|
|
for token, meta in scanned_disease_items
|
|
],
|
|
eval_split=eval_split,
|
|
tau=tau,
|
|
landmark_start=float(args.landmark_start),
|
|
landmark_stop=float(args.landmark_stop),
|
|
landmark_step=float(args.landmark_step),
|
|
)
|
|
print(f"Wrote {written_rows} rows in {len(shards)} shard(s) to {output_dir}")
|
|
print(f"Wrote {summary_rows} summary rows to {summary_path}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|