Add evaluation scripts for next-event prediction and horizon-capture evaluation with detailed metric disclaimers
This commit is contained in:
38
README.md
38
README.md
@@ -1,2 +1,40 @@
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# DeepHealth
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## Evaluation
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This repo includes two event-driven evaluation entrypoints:
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- `evaluate_next_event.py`: next-event prediction using short-window CIF
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- `evaluate_horizon.py`: horizon-capture evaluation using CIF at multiple horizons
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### IMPORTANT metric disclaimers
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- **AUC** reported by `evaluate_horizon.py` is “time-dependent” only because the label depends on the chosen horizon $\tau$.
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Without explicit follow-up end times / censoring, this is **not** a classical risk-set AUC with IPCW.
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Use it for **model comparison and diagnostics**, not strict statistical interpretation.
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- **Brier score** reported by `evaluate_horizon.py` is an unadjusted diagnostic/proxy metric (no censoring adjustment).
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Use it to detect probability-mass compression / numerical stability issues; do not claim calibrated absolute risk.
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### Example
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```bash
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# Next-event (no --horizons)
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python evaluate_next_event.py \
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--run_dir runs/your_run \
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--tau_short 0.25 \
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--age_bins 40 45 50 55 60 65 70 inf \
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--device cuda \
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--batch_size 256 \
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--seed 0
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# Horizon-capture
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python evaluate_horizon.py \
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--run_dir runs/your_run \
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--horizons 0.25 0.5 1.0 2.0 5.0 10.0 \
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--age_bins 40 45 50 55 60 65 70 inf \
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--device cuda \
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--batch_size 256 \
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--seed 0
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```
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277
evaluate_horizon.py
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277
evaluate_horizon.py
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"""Horizon-capture evaluation.
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DISCLAIMERS (important):
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- The reported AUC is "time-dependent" only because the label depends on the chosen horizon $\tau$.
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Without explicit censoring / follow-up end times, this is NOT a classical risk-set AUC with IPCW.
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Use it for model comparison and diagnostics, not strict statistical interpretation.
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- The reported Brier scores are unadjusted diagnostic/proxy metrics (no censoring adjustment).
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Use them to detect probability-mass compression / numerical stability issues; do not claim
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calibrated absolute risk.
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"""
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import argparse
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import os
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from typing import Dict, List, 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.utils.data import DataLoader
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from utils import (
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EvalRecordDataset,
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build_dataset_from_config,
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build_event_driven_records,
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build_model_head_criterion,
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eval_collate_fn,
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flatten_future_events,
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get_test_subset,
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load_checkpoint_into,
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load_train_config,
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make_inference_dataloader_kwargs,
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parse_float_list,
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predict_cifs,
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roc_auc_ovr,
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seed_everything,
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topk_indices,
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)
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def parse_args() -> argparse.Namespace:
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p = argparse.ArgumentParser(
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description="Evaluate horizon-capture using CIF at horizons")
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p.add_argument("--run_dir", type=str, required=True)
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p.add_argument(
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"--horizons",
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type=str,
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nargs="+",
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default=["0.25", "0.5", "1.0", "2.0", "5.0", "10.0"],
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help="Horizon grid in years",
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)
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p.add_argument(
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"--age_bins",
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type=str,
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nargs="+",
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default=["40", "45", "50", "55", "60", "65", "70", "75", "inf"],
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help="Age bin boundaries in years (default: 40 45 50 55 60 65 70 75 inf)",
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)
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p.add_argument(
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"--device",
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type=str,
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default=("cuda" if torch.cuda.is_available() else "cpu"),
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)
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p.add_argument("--batch_size", type=int, default=256)
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p.add_argument("--num_workers", type=int, default=0)
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p.add_argument("--seed", type=int, default=0)
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p.add_argument("--min_pos", type=int, default=20)
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p.add_argument(
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"--topk_list",
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type=int,
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nargs="+",
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default=[1, 5, 10, 20, 50],
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)
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return p.parse_args()
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def build_labels_within_tau_flat(
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n_records: int,
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n_causes: int,
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event_record_idx: np.ndarray,
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event_cause: np.ndarray,
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event_dt_years: np.ndarray,
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tau_years: float,
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) -> np.ndarray:
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"""Build y_within_tau using a flattened (record,cause,dt) representation.
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This preserves the exact label definition: y[i,k]=1 iff at least one event of cause k
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occurs in (t0, t0+tau].
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"""
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y = np.zeros((n_records, n_causes), dtype=np.int8)
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if event_dt_years.size == 0:
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return y
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m = event_dt_years <= float(tau_years)
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if not np.any(m):
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return y
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y[event_record_idx[m], event_cause[m]] = 1
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return y
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def main() -> None:
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args = parse_args()
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seed_everything(args.seed)
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run_dir = args.run_dir
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cfg = load_train_config(run_dir)
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dataset = build_dataset_from_config(cfg)
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test_subset = get_test_subset(dataset, cfg)
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age_bins_years = parse_float_list(args.age_bins)
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horizons = parse_float_list(args.horizons)
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horizons = [float(h) for h in horizons]
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records = build_event_driven_records(
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dataset=dataset,
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subset=test_subset,
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age_bins_years=age_bins_years,
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seed=args.seed,
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)
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device = torch.device(args.device)
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model, head, criterion = build_model_head_criterion(cfg, dataset, device)
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load_checkpoint_into(run_dir, model, head, criterion, device)
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rec_ds = EvalRecordDataset(dataset, records)
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dl_kwargs = make_inference_dataloader_kwargs(device, args.num_workers)
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loader = DataLoader(
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rec_ds,
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batch_size=args.batch_size,
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shuffle=False,
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num_workers=args.num_workers,
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collate_fn=eval_collate_fn,
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**dl_kwargs,
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)
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# Print disclaimers every run (requested)
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print("DISCLAIMER: AUC here is horizon-dependent label AUC (no IPCW / censoring adjustment).")
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print("DISCLAIMER: Brier is unadjusted diagnostic/proxy (no censoring adjustment).")
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scores = predict_cifs(model, head, criterion, loader,
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horizons, device=device)
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# scores shape: (N, K, H)
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if scores.ndim != 3:
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raise ValueError(
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f"Expected CIF scores with shape (N,K,H), got {scores.shape}")
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N, K, H = scores.shape
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if N != len(records):
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raise ValueError("Record count mismatch")
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# Pre-flatten all future events once to avoid repeated per-record scans.
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evt_rec_idx, evt_cause, evt_dt = flatten_future_events(records, n_causes=K)
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per_tau_rows: List[Dict[str, object]] = []
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per_cause_rows: List[Dict[str, object]] = []
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workload_rows: List[Dict[str, object]] = []
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for h_idx, tau in enumerate(horizons):
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s_tau = scores[:, :, h_idx]
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y_tau = build_labels_within_tau_flat(
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N, K, evt_rec_idx, evt_cause, evt_dt, tau)
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# Per-cause counts + Brier (vectorized)
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n_pos = y_tau.sum(axis=0).astype(np.int64)
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n_neg = (int(N) - n_pos).astype(np.int64)
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# Brier per cause: mean_i (y - s)^2
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brier_per_cause = np.mean(
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(y_tau.astype(np.float64) - s_tau.astype(np.float64)) ** 2, axis=0)
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brier_macro = float(np.mean(brier_per_cause)) if K > 0 else float("nan")
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brier_weighted = float(np.sum(
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brier_per_cause * n_pos) / np.sum(n_pos)) if np.sum(n_pos) > 0 else float("nan")
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# AUC: compute only for causes with enough positives and at least 1 negative
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auc = np.full((K,), np.nan, dtype=np.float64)
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min_pos = int(args.min_pos)
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candidates = np.flatnonzero((n_pos >= min_pos) & (n_neg > 0))
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for k in candidates:
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auc[k] = roc_auc_ovr(y_tau[:, k].astype(
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np.int32), s_tau[:, k].astype(np.float64))
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finite_auc = auc[np.isfinite(auc)]
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auc_macro = float(np.mean(finite_auc)
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) if finite_auc.size > 0 else float("nan")
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w_mask = np.isfinite(auc)
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auc_weighted = float(np.sum(auc[w_mask] * n_pos[w_mask]) / np.sum(
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n_pos[w_mask])) if np.sum(n_pos[w_mask]) > 0 else float("nan")
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n_valid_auc = int(np.isfinite(auc).sum())
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# Append per-cause rows (vectorized via DataFrame to avoid Python loops)
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per_cause_rows.append(
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pd.DataFrame(
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{
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"tau_years": float(tau),
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"cause_id": np.arange(K, dtype=np.int64),
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"n_pos": n_pos,
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"n_neg": n_neg,
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"auc": auc,
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"brier": brier_per_cause,
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}
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)
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)
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# Business metrics for each topK
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denom_true_pairs = int(y_tau.sum())
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for topk in args.topk_list:
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topk = int(topk)
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idx = topk_indices(s_tau, topk)
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captured = np.take_along_axis(y_tau, idx, axis=1)
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hits = captured.sum(axis=1).astype(np.float64)
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true_cnt = y_tau.sum(axis=1).astype(np.float64)
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precision_like = hits / float(min(topk, K))
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mean_precision = float(np.mean(precision_like)
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) if N > 0 else float("nan")
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mask_has_true = true_cnt > 0
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recall_like = np.full((N,), np.nan, dtype=np.float64)
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recall_like[mask_has_true] = hits[mask_has_true] / \
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true_cnt[mask_has_true]
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mean_recall = float(np.nanmean(recall_like)) if np.any(
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mask_has_true) else float("nan")
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median_recall = float(np.nanmedian(recall_like)) if np.any(
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mask_has_true) else float("nan")
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numer_captured_pairs = int(captured.sum())
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pop_capture_rate = float(
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numer_captured_pairs / denom_true_pairs) if denom_true_pairs > 0 else float("nan")
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workload_rows.append(
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{
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"tau_years": float(tau),
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"topk": int(topk),
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"population_capture_rate": pop_capture_rate,
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"mean_precision_like": mean_precision,
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"mean_recall_like": mean_recall,
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"median_recall_like": median_recall,
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"denom_true_pairs": denom_true_pairs,
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"numer_captured_pairs": numer_captured_pairs,
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}
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)
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per_tau_rows.append(
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{
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"tau_years": float(tau),
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"n_records": int(N),
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"n_causes": int(K),
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"auc_macro": auc_macro,
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"auc_weighted_by_npos": auc_weighted,
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"n_causes_valid_auc": int(n_valid_auc),
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"brier_macro": brier_macro,
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"brier_weighted_by_npos": brier_weighted,
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"total_true_pairs": denom_true_pairs,
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}
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)
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out_metrics = os.path.join(run_dir, "horizon_metrics.csv")
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out_pc = os.path.join(run_dir, "horizon_per_cause.csv")
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out_wy = os.path.join(run_dir, "workload_yield.csv")
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pd.DataFrame(per_tau_rows).to_csv(out_metrics, index=False)
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if per_cause_rows:
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pd.concat(per_cause_rows, ignore_index=True).to_csv(out_pc, index=False)
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else:
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pd.DataFrame(columns=["tau_years", "cause_id", "n_pos",
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"n_neg", "auc", "brier"]).to_csv(out_pc, index=False)
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pd.DataFrame(workload_rows).to_csv(out_wy, index=False)
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print(f"Wrote {out_metrics}")
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print(f"Wrote {out_pc}")
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print(f"Wrote {out_wy}")
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if __name__ == "__main__":
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main()
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188
evaluate_next_event.py
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188
evaluate_next_event.py
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@@ -0,0 +1,188 @@
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import argparse
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import os
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from typing import List
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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 utils import (
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EvalRecordDataset,
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build_dataset_from_config,
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build_event_driven_records,
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build_model_head_criterion,
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eval_collate_fn,
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get_test_subset,
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make_inference_dataloader_kwargs,
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load_checkpoint_into,
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load_train_config,
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parse_float_list,
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predict_cifs,
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roc_auc_ovr,
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seed_everything,
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topk_indices,
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)
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def parse_args() -> argparse.Namespace:
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p = argparse.ArgumentParser(
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description="Evaluate next-event prediction using short-window CIF"
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)
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p.add_argument("--run_dir", type=str, required=True)
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p.add_argument("--tau_short", type=float, required=True, help="years")
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p.add_argument(
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"--age_bins",
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type=str,
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nargs="+",
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default=["40", "45", "50", "55", "60", "65", "70", "75", "inf"],
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help="Age bin boundaries in years (default: 40 45 50 55 60 65 70 75 inf)",
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)
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p.add_argument(
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"--device",
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type=str,
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default=("cuda" if torch.cuda.is_available() else "cpu"),
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)
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p.add_argument("--batch_size", type=int, default=256)
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p.add_argument("--num_workers", type=int, default=0)
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p.add_argument("--seed", type=int, default=0)
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p.add_argument(
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"--min_pos",
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type=int,
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default=20,
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help="Minimum positives for per-cause AUC",
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)
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return p.parse_args()
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def main() -> None:
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args = parse_args()
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seed_everything(args.seed)
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run_dir = args.run_dir
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cfg = load_train_config(run_dir)
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dataset = build_dataset_from_config(cfg)
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test_subset = get_test_subset(dataset, cfg)
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age_bins_years = parse_float_list(args.age_bins)
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records = build_event_driven_records(
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dataset=dataset,
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subset=test_subset,
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age_bins_years=age_bins_years,
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seed=args.seed,
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)
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device = torch.device(args.device)
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model, head, criterion = build_model_head_criterion(cfg, dataset, device)
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load_checkpoint_into(run_dir, model, head, criterion, device)
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rec_ds = EvalRecordDataset(dataset, records)
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dl_kwargs = make_inference_dataloader_kwargs(device, args.num_workers)
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loader = DataLoader(
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rec_ds,
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batch_size=args.batch_size,
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shuffle=False,
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num_workers=args.num_workers,
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collate_fn=eval_collate_fn,
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**dl_kwargs,
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)
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tau = float(args.tau_short)
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scores = predict_cifs(model, head, criterion, loader, [tau], device=device)
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# scores shape: (N,K,1) for multi-taus; squeeze last
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if scores.ndim == 3:
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scores = scores[:, :, 0]
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n_records_total = len(records)
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y_next = np.array(
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[(-1 if r.next_event_cause is None else int(r.next_event_cause))
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for r in records],
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dtype=np.int64,
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)
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eligible = y_next >= 0
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n_eligible = int(eligible.sum())
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coverage = float(
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n_eligible / n_records_total) if n_records_total > 0 else 0.0
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metrics_rows: List[dict] = []
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metrics_rows.append({"metric": "n_records_total", "value": n_records_total})
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metrics_rows.append(
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{"metric": "n_next_event_eligible", "value": n_eligible})
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metrics_rows.append({"metric": "coverage", "value": coverage})
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metrics_rows.append({"metric": "tau_short_years", "value": tau})
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if n_eligible == 0:
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out_path = os.path.join(run_dir, "next_event_metrics.csv")
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pd.DataFrame(metrics_rows).to_csv(out_path, index=False)
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print(f"No eligible records; wrote {out_path}")
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return
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scores_e = scores[eligible]
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y_e = y_next[eligible]
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pred = scores_e.argmax(axis=1)
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acc = float((pred == y_e).mean())
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metrics_rows.append({"metric": "top1_accuracy", "value": acc})
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# MRR
|
||||
order = np.argsort(-scores_e, axis=1, kind="mergesort")
|
||||
ranks = np.empty(y_e.shape[0], dtype=np.int32)
|
||||
for i in range(y_e.shape[0]):
|
||||
ranks[i] = int(np.where(order[i] == y_e[i])[0][0]) + 1
|
||||
mrr = float((1.0 / ranks).mean())
|
||||
metrics_rows.append({"metric": "mrr", "value": mrr})
|
||||
|
||||
# HitRate@K
|
||||
for k in [1, 3, 5, 10, 20]:
|
||||
topk = topk_indices(scores_e, k)
|
||||
hit = (topk == y_e[:, None]).any(axis=1)
|
||||
metrics_rows.append({"metric": f"hitrate_at_{k}",
|
||||
"value": float(hit.mean())})
|
||||
|
||||
# Macro OvR AUC per cause (optional)
|
||||
K = scores.shape[1]
|
||||
n_pos = np.bincount(y_e, minlength=K).astype(np.int64)
|
||||
n_neg = (int(y_e.size) - n_pos).astype(np.int64)
|
||||
|
||||
auc = np.full((K,), np.nan, dtype=np.float64)
|
||||
min_pos = int(args.min_pos)
|
||||
candidates = np.flatnonzero((n_pos >= min_pos) & (n_neg > 0))
|
||||
for k in candidates:
|
||||
auc_k = roc_auc_ovr((y_e == k).astype(np.int32), scores_e[:, k])
|
||||
auc[k] = auc_k
|
||||
|
||||
included = (n_pos >= min_pos) & (n_neg > 0)
|
||||
per_cause_df = pd.DataFrame(
|
||||
{
|
||||
"cause_id": np.arange(K, dtype=np.int64),
|
||||
"n_pos": n_pos,
|
||||
"n_neg": n_neg,
|
||||
"auc": auc,
|
||||
"included": included,
|
||||
}
|
||||
)
|
||||
|
||||
aucs = auc[np.isfinite(auc)]
|
||||
|
||||
if aucs:
|
||||
metrics_rows.append(
|
||||
{"metric": "macro_ovr_auc", "value": float(np.mean(aucs))})
|
||||
else:
|
||||
metrics_rows.append({"metric": "macro_ovr_auc", "value": float("nan")})
|
||||
|
||||
out_metrics = os.path.join(run_dir, "next_event_metrics.csv")
|
||||
pd.DataFrame(metrics_rows).to_csv(out_metrics, index=False)
|
||||
|
||||
# optional per-cause
|
||||
out_pc = os.path.join(run_dir, "next_event_per_cause.csv")
|
||||
per_cause_df.to_csv(out_pc, index=False)
|
||||
|
||||
print(f"Wrote {out_metrics}")
|
||||
print(f"Wrote {out_pc}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
566
utils.py
Normal file
566
utils.py
Normal file
@@ -0,0 +1,566 @@
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import re
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Sequence, Tuple
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch.utils.data import DataLoader, Dataset, Subset, random_split
|
||||
|
||||
from dataset import HealthDataset
|
||||
from losses import (
|
||||
DiscreteTimeCIFNLLLoss,
|
||||
ExponentialNLLLoss,
|
||||
PiecewiseExponentialCIFNLLLoss,
|
||||
)
|
||||
from model import DelphiFork, SapDelphi, SimpleHead
|
||||
|
||||
|
||||
DAYS_PER_YEAR = 365.25
|
||||
N_TECH_TOKENS = 2 # pad=0, DOA=1, diseases start at 2
|
||||
|
||||
|
||||
def make_inference_dataloader_kwargs(
|
||||
device: torch.device,
|
||||
num_workers: int,
|
||||
) -> Dict[str, Any]:
|
||||
"""DataLoader kwargs tuned for inference throughput.
|
||||
|
||||
Behavior/metrics are unchanged; this only impacts speed.
|
||||
"""
|
||||
use_cuda = device.type == "cuda" and torch.cuda.is_available()
|
||||
kwargs: Dict[str, Any] = {
|
||||
"pin_memory": bool(use_cuda),
|
||||
}
|
||||
if num_workers > 0:
|
||||
kwargs["persistent_workers"] = True
|
||||
# default prefetch is 2; set explicitly for clarity.
|
||||
kwargs["prefetch_factor"] = 2
|
||||
return kwargs
|
||||
|
||||
|
||||
# -------------------------
|
||||
# Config + determinism
|
||||
# -------------------------
|
||||
|
||||
def _replace_nonstandard_json_numbers(text: str) -> str:
|
||||
# Python's json.dump writes Infinity/-Infinity/NaN for non-finite floats.
|
||||
# Replace bare tokens (not within quotes) with string placeholders.
|
||||
def repl(match: re.Match[str]) -> str:
|
||||
token = match.group(0)
|
||||
if token == "-Infinity":
|
||||
return '"__NINF__"'
|
||||
if token == "Infinity":
|
||||
return '"__INF__"'
|
||||
if token == "NaN":
|
||||
return '"__NAN__"'
|
||||
return token
|
||||
|
||||
return re.sub(r'(?<![\w\."])(-Infinity|Infinity|NaN)(?![\w\."])', repl, text)
|
||||
|
||||
|
||||
def _restore_placeholders(obj: Any) -> Any:
|
||||
if isinstance(obj, dict):
|
||||
return {k: _restore_placeholders(v) for k, v in obj.items()}
|
||||
if isinstance(obj, list):
|
||||
return [_restore_placeholders(v) for v in obj]
|
||||
if obj == "__INF__":
|
||||
return float("inf")
|
||||
if obj == "__NINF__":
|
||||
return float("-inf")
|
||||
if obj == "__NAN__":
|
||||
return float("nan")
|
||||
return obj
|
||||
|
||||
|
||||
def load_train_config(run_dir: str) -> Dict[str, Any]:
|
||||
cfg_path = os.path.join(run_dir, "train_config.json")
|
||||
with open(cfg_path, "r", encoding="utf-8") as f:
|
||||
raw = f.read()
|
||||
raw = _replace_nonstandard_json_numbers(raw)
|
||||
cfg = json.loads(raw)
|
||||
cfg = _restore_placeholders(cfg)
|
||||
return cfg
|
||||
|
||||
|
||||
def seed_everything(seed: int) -> None:
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
|
||||
|
||||
def parse_float_list(values: Sequence[str]) -> List[float]:
|
||||
out: List[float] = []
|
||||
for v in values:
|
||||
s = str(v).strip().lower()
|
||||
if s in {"inf", "+inf", "infty", "infinity", "+infinity"}:
|
||||
out.append(float("inf"))
|
||||
elif s in {"-inf", "-infty", "-infinity"}:
|
||||
out.append(float("-inf"))
|
||||
else:
|
||||
out.append(float(v))
|
||||
return out
|
||||
|
||||
|
||||
# -------------------------
|
||||
# Dataset + split (match train.py)
|
||||
# -------------------------
|
||||
|
||||
def build_dataset_from_config(cfg: Dict[str, Any]) -> HealthDataset:
|
||||
data_prefix = cfg["data_prefix"]
|
||||
full_cov = bool(cfg.get("full_cov", False))
|
||||
|
||||
if full_cov:
|
||||
cov_list = None
|
||||
else:
|
||||
cov_list = ["bmi", "smoking", "alcohol"]
|
||||
|
||||
dataset = HealthDataset(
|
||||
data_prefix=data_prefix,
|
||||
covariate_list=cov_list,
|
||||
)
|
||||
return dataset
|
||||
|
||||
|
||||
def get_test_subset(dataset: HealthDataset, cfg: Dict[str, Any]) -> Subset:
|
||||
n_total = len(dataset)
|
||||
train_ratio = float(cfg["train_ratio"])
|
||||
val_ratio = float(cfg["val_ratio"])
|
||||
seed = int(cfg["random_seed"])
|
||||
|
||||
n_train = int(n_total * train_ratio)
|
||||
n_val = int(n_total * val_ratio)
|
||||
n_test = n_total - n_train - n_val
|
||||
|
||||
_, _, test_subset = random_split(
|
||||
dataset,
|
||||
[n_train, n_val, n_test],
|
||||
generator=torch.Generator().manual_seed(seed),
|
||||
)
|
||||
return test_subset
|
||||
|
||||
|
||||
# -------------------------
|
||||
# Model + head + criterion (match train.py)
|
||||
# -------------------------
|
||||
|
||||
def build_model_head_criterion(
|
||||
cfg: Dict[str, Any],
|
||||
dataset: HealthDataset,
|
||||
device: torch.device,
|
||||
) -> Tuple[torch.nn.Module, torch.nn.Module, torch.nn.Module]:
|
||||
loss_type = cfg["loss_type"]
|
||||
|
||||
if loss_type == "exponential":
|
||||
criterion = ExponentialNLLLoss(lambda_reg=float(
|
||||
cfg.get("lambda_reg", 0.0))).to(device)
|
||||
out_dims = [dataset.n_disease]
|
||||
elif loss_type == "discrete_time_cif":
|
||||
bin_edges = [float(x) for x in cfg["bin_edges"]]
|
||||
criterion = DiscreteTimeCIFNLLLoss(
|
||||
bin_edges=bin_edges,
|
||||
lambda_reg=float(cfg.get("lambda_reg", 0.0)),
|
||||
).to(device)
|
||||
out_dims = [dataset.n_disease + 1, len(bin_edges)]
|
||||
elif loss_type == "pwe_cif":
|
||||
# training drops +inf for PWE
|
||||
raw_edges = [float(x) for x in cfg["bin_edges"]]
|
||||
pwe_edges = [float(x) for x in raw_edges if math.isfinite(float(x))]
|
||||
if len(pwe_edges) < 2:
|
||||
raise ValueError(
|
||||
"pwe_cif requires at least 2 finite bin edges (including 0). "
|
||||
f"Got bin_edges={raw_edges}"
|
||||
)
|
||||
if float(pwe_edges[0]) != 0.0:
|
||||
raise ValueError(
|
||||
f"pwe_cif requires bin_edges[0]==0.0; got {pwe_edges[0]}")
|
||||
|
||||
criterion = PiecewiseExponentialCIFNLLLoss(
|
||||
bin_edges=pwe_edges,
|
||||
lambda_reg=float(cfg.get("lambda_reg", 0.0)),
|
||||
).to(device)
|
||||
n_bins = len(pwe_edges) - 1
|
||||
out_dims = [dataset.n_disease, n_bins]
|
||||
else:
|
||||
raise ValueError(f"Unsupported loss_type: {loss_type}")
|
||||
|
||||
model_type = cfg["model_type"]
|
||||
if model_type == "delphi_fork":
|
||||
model = DelphiFork(
|
||||
n_disease=dataset.n_disease,
|
||||
n_tech_tokens=N_TECH_TOKENS,
|
||||
n_embd=int(cfg["n_embd"]),
|
||||
n_head=int(cfg["n_head"]),
|
||||
n_layer=int(cfg["n_layer"]),
|
||||
pdrop=float(cfg.get("pdrop", 0.0)),
|
||||
age_encoder_type=str(cfg.get("age_encoder", "sinusoidal")),
|
||||
n_cont=int(dataset.n_cont),
|
||||
n_cate=int(dataset.n_cate),
|
||||
cate_dims=list(dataset.cate_dims),
|
||||
).to(device)
|
||||
elif model_type == "sap_delphi":
|
||||
model = SapDelphi(
|
||||
n_disease=dataset.n_disease,
|
||||
n_tech_tokens=N_TECH_TOKENS,
|
||||
n_embd=int(cfg["n_embd"]),
|
||||
n_head=int(cfg["n_head"]),
|
||||
n_layer=int(cfg["n_layer"]),
|
||||
pdrop=float(cfg.get("pdrop", 0.0)),
|
||||
age_encoder_type=str(cfg.get("age_encoder", "sinusoidal")),
|
||||
n_cont=int(dataset.n_cont),
|
||||
n_cate=int(dataset.n_cate),
|
||||
cate_dims=list(dataset.cate_dims),
|
||||
pretrained_weights_path=str(
|
||||
cfg.get("pretrained_emd_path", "icd10_sapbert_embeddings.npy")),
|
||||
freeze_embeddings=True,
|
||||
).to(device)
|
||||
else:
|
||||
raise ValueError(f"Unsupported model_type: {model_type}")
|
||||
|
||||
head = SimpleHead(
|
||||
n_embd=int(cfg["n_embd"]),
|
||||
out_dims=list(out_dims),
|
||||
).to(device)
|
||||
|
||||
return model, head, criterion
|
||||
|
||||
|
||||
def load_checkpoint_into(
|
||||
run_dir: str,
|
||||
model: torch.nn.Module,
|
||||
head: torch.nn.Module,
|
||||
criterion: Optional[torch.nn.Module],
|
||||
device: torch.device,
|
||||
) -> Dict[str, Any]:
|
||||
ckpt_path = os.path.join(run_dir, "best_model.pt")
|
||||
ckpt = torch.load(ckpt_path, map_location=device)
|
||||
model.load_state_dict(ckpt["model_state_dict"], strict=True)
|
||||
head.load_state_dict(ckpt["head_state_dict"], strict=True)
|
||||
if criterion is not None and "criterion_state_dict" in ckpt:
|
||||
try:
|
||||
criterion.load_state_dict(
|
||||
ckpt["criterion_state_dict"], strict=False)
|
||||
except Exception:
|
||||
# Criterion state is not essential for inference.
|
||||
pass
|
||||
return ckpt
|
||||
|
||||
|
||||
# -------------------------
|
||||
# Evaluation record construction (event-driven)
|
||||
# -------------------------
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class EvalRecord:
|
||||
patient_idx: int
|
||||
patient_id: Any
|
||||
doa_days: float
|
||||
t0_days: float
|
||||
cutoff_pos: int # baseline position (inclusive)
|
||||
next_event_cause: Optional[int]
|
||||
next_event_dt_years: Optional[float]
|
||||
future_causes: np.ndarray # (E,) in [0..K-1]
|
||||
future_dt_years: np.ndarray # (E,) strictly > 0
|
||||
|
||||
|
||||
def _to_days(x_years: float) -> float:
|
||||
if math.isinf(float(x_years)):
|
||||
return float("inf")
|
||||
return float(x_years) * DAYS_PER_YEAR
|
||||
|
||||
|
||||
def build_event_driven_records(
|
||||
dataset: HealthDataset,
|
||||
subset: Subset,
|
||||
age_bins_years: Sequence[float],
|
||||
seed: int,
|
||||
) -> List[EvalRecord]:
|
||||
if len(age_bins_years) < 2:
|
||||
raise ValueError("age_bins must have at least 2 boundaries")
|
||||
|
||||
age_bins_days = [_to_days(b) for b in age_bins_years]
|
||||
if any(age_bins_days[i] >= age_bins_days[i + 1] for i in range(len(age_bins_days) - 1)):
|
||||
raise ValueError("age_bins must be strictly increasing")
|
||||
|
||||
rng = np.random.default_rng(seed)
|
||||
|
||||
records: List[EvalRecord] = []
|
||||
|
||||
# Subset.indices is deterministic from random_split
|
||||
indices = list(getattr(subset, "indices", range(len(subset))))
|
||||
|
||||
# Speed: avoid calling dataset.__getitem__ for every patient here.
|
||||
# We only need DOA + event times/codes to create evaluation records.
|
||||
eps = 1e-6
|
||||
for patient_idx in indices:
|
||||
patient_id = dataset.patient_ids[patient_idx]
|
||||
|
||||
doa_days = float(dataset._doa[patient_idx])
|
||||
|
||||
raw_records = dataset.patient_events.get(patient_id, [])
|
||||
if raw_records:
|
||||
times = np.asarray([t for t, _ in raw_records], dtype=np.float64)
|
||||
codes = np.asarray([c for _, c in raw_records], dtype=np.int64)
|
||||
else:
|
||||
times = np.zeros((0,), dtype=np.float64)
|
||||
codes = np.zeros((0,), dtype=np.int64)
|
||||
|
||||
# Mirror HealthDataset insertion logic exactly.
|
||||
insert_pos = int(np.searchsorted(times, doa_days, side="left"))
|
||||
times_ins = np.insert(times, insert_pos, doa_days)
|
||||
codes_ins = np.insert(codes, insert_pos, 1)
|
||||
|
||||
is_disease = codes_ins >= N_TECH_TOKENS
|
||||
disease_times = times_ins[is_disease]
|
||||
|
||||
for b in range(len(age_bins_days) - 1):
|
||||
lo = age_bins_days[b]
|
||||
hi = age_bins_days[b + 1]
|
||||
|
||||
# Inclusion rule:
|
||||
# 1) DOA <= bin_upper
|
||||
if not (doa_days <= hi):
|
||||
continue
|
||||
|
||||
# 2) at least one disease event within bin, and baseline must satisfy t0>=DOA
|
||||
in_bin = (disease_times >= lo) & (
|
||||
disease_times < hi) & (disease_times >= doa_days)
|
||||
cand_times = disease_times[in_bin]
|
||||
if cand_times.size == 0:
|
||||
continue
|
||||
|
||||
t0_days = float(rng.choice(cand_times))
|
||||
|
||||
# Baseline position (inclusive) in the *post-DOA-inserted* sequence.
|
||||
pos = np.flatnonzero(is_disease & np.isclose(
|
||||
times_ins, t0_days, rtol=0.0, atol=eps))
|
||||
if pos.size == 0:
|
||||
disease_pos = np.flatnonzero(is_disease)
|
||||
if disease_pos.size == 0:
|
||||
continue
|
||||
disease_times_full = times_ins[disease_pos]
|
||||
closest_idx = int(
|
||||
np.argmin(np.abs(disease_times_full - t0_days)))
|
||||
cutoff_pos = int(disease_pos[closest_idx])
|
||||
t0_days = float(disease_times_full[closest_idx])
|
||||
else:
|
||||
cutoff_pos = int(pos[0])
|
||||
|
||||
# Future disease events strictly after t0
|
||||
future_mask = (times_ins > (t0_days + eps)) & is_disease
|
||||
future_pos = np.flatnonzero(future_mask)
|
||||
if future_pos.size == 0:
|
||||
next_cause = None
|
||||
next_dt_years = None
|
||||
future_causes = np.zeros((0,), dtype=np.int64)
|
||||
future_dt_years_arr = np.zeros((0,), dtype=np.float32)
|
||||
else:
|
||||
future_times_days = times_ins[future_pos]
|
||||
future_tokens = codes_ins[future_pos]
|
||||
future_causes = (future_tokens - N_TECH_TOKENS).astype(np.int64)
|
||||
future_dt_years_arr = (
|
||||
(future_times_days - t0_days) / DAYS_PER_YEAR).astype(np.float32)
|
||||
|
||||
# next-event = minimal time > t0 (tie broken by earliest position)
|
||||
next_idx = int(np.argmin(future_times_days))
|
||||
next_cause = int(future_causes[next_idx])
|
||||
next_dt_years = float(future_dt_years_arr[next_idx])
|
||||
|
||||
records.append(
|
||||
EvalRecord(
|
||||
patient_idx=int(patient_idx),
|
||||
patient_id=patient_id,
|
||||
doa_days=float(doa_days),
|
||||
t0_days=float(t0_days),
|
||||
cutoff_pos=int(cutoff_pos),
|
||||
next_event_cause=next_cause,
|
||||
next_event_dt_years=next_dt_years,
|
||||
future_causes=future_causes,
|
||||
future_dt_years=future_dt_years_arr,
|
||||
)
|
||||
)
|
||||
|
||||
return records
|
||||
|
||||
|
||||
class EvalRecordDataset(Dataset):
|
||||
def __init__(self, base_dataset: HealthDataset, records: Sequence[EvalRecord]):
|
||||
self.base = base_dataset
|
||||
self.records = list(records)
|
||||
self._cache: Dict[int, Tuple[torch.Tensor,
|
||||
torch.Tensor, torch.Tensor, torch.Tensor, int]] = {}
|
||||
self._cache_order: List[int] = []
|
||||
self._cache_max = 2048
|
||||
|
||||
def __len__(self) -> int:
|
||||
return len(self.records)
|
||||
|
||||
def __getitem__(self, idx: int):
|
||||
rec = self.records[idx]
|
||||
cached = self._cache.get(rec.patient_idx)
|
||||
if cached is None:
|
||||
event_seq, time_seq, cont, cate, sex = self.base[rec.patient_idx]
|
||||
cached = (event_seq, time_seq, cont, cate, int(sex))
|
||||
self._cache[rec.patient_idx] = cached
|
||||
self._cache_order.append(rec.patient_idx)
|
||||
if len(self._cache_order) > self._cache_max:
|
||||
drop = self._cache_order.pop(0)
|
||||
self._cache.pop(drop, None)
|
||||
else:
|
||||
event_seq, time_seq, cont, cate, sex = cached
|
||||
cutoff = rec.cutoff_pos + 1
|
||||
event_seq = event_seq[:cutoff]
|
||||
time_seq = time_seq[:cutoff]
|
||||
baseline_pos = rec.cutoff_pos # same index in truncated sequence
|
||||
return event_seq, time_seq, cont, cate, sex, baseline_pos
|
||||
|
||||
|
||||
def eval_collate_fn(batch):
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
|
||||
event_seqs, time_seqs, cont_feats, cate_feats, sexes, baseline_pos = zip(
|
||||
*batch)
|
||||
event_batch = pad_sequence(event_seqs, batch_first=True, padding_value=0)
|
||||
time_batch = pad_sequence(
|
||||
time_seqs, batch_first=True, padding_value=36525.0)
|
||||
cont_batch = torch.stack(cont_feats, dim=0).unsqueeze(1)
|
||||
cate_batch = torch.stack(cate_feats, dim=0).unsqueeze(1)
|
||||
sex_batch = torch.tensor(sexes, dtype=torch.long)
|
||||
baseline_pos = torch.tensor(baseline_pos, dtype=torch.long)
|
||||
return event_batch, time_batch, cont_batch, cate_batch, sex_batch, baseline_pos
|
||||
|
||||
|
||||
# -------------------------
|
||||
# Inference utilities
|
||||
# -------------------------
|
||||
|
||||
def predict_cifs(
|
||||
model: torch.nn.Module,
|
||||
head: torch.nn.Module,
|
||||
criterion: torch.nn.Module,
|
||||
loader: DataLoader,
|
||||
taus_years: Sequence[float],
|
||||
device: torch.device,
|
||||
) -> np.ndarray:
|
||||
model.eval()
|
||||
head.eval()
|
||||
|
||||
taus_t = torch.tensor(list(taus_years), dtype=torch.float32, device=device)
|
||||
|
||||
all_out: List[np.ndarray] = []
|
||||
with torch.no_grad():
|
||||
for batch in loader:
|
||||
event_seq, time_seq, cont, cate, sex, baseline_pos = batch
|
||||
event_seq = event_seq.to(device, non_blocking=True)
|
||||
time_seq = time_seq.to(device, non_blocking=True)
|
||||
cont = cont.to(device, non_blocking=True)
|
||||
cate = cate.to(device, non_blocking=True)
|
||||
sex = sex.to(device, non_blocking=True)
|
||||
baseline_pos = baseline_pos.to(device, non_blocking=True)
|
||||
|
||||
h = model(event_seq, time_seq, sex, cont, cate)
|
||||
b_idx = torch.arange(h.size(0), device=device)
|
||||
c = h[b_idx, baseline_pos]
|
||||
logits = head(c)
|
||||
|
||||
cifs = criterion.calculate_cifs(logits, taus_t)
|
||||
out = cifs.detach().cpu().numpy()
|
||||
all_out.append(out)
|
||||
|
||||
return np.concatenate(all_out, axis=0) if all_out else np.zeros((0,))
|
||||
|
||||
|
||||
def flatten_future_events(
|
||||
records: Sequence[EvalRecord],
|
||||
n_causes: int,
|
||||
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
"""Flatten (record_idx, cause, dt_years) across all future events.
|
||||
|
||||
Used to build horizon labels via vectorized masking + scatter.
|
||||
"""
|
||||
rec_idx_parts: List[np.ndarray] = []
|
||||
cause_parts: List[np.ndarray] = []
|
||||
dt_parts: List[np.ndarray] = []
|
||||
|
||||
for i, r in enumerate(records):
|
||||
if r.future_causes.size == 0:
|
||||
continue
|
||||
causes = r.future_causes
|
||||
dts = r.future_dt_years
|
||||
# Keep only valid cause ids.
|
||||
m = (causes >= 0) & (causes < n_causes)
|
||||
if not np.any(m):
|
||||
continue
|
||||
causes = causes[m].astype(np.int64, copy=False)
|
||||
dts = dts[m].astype(np.float32, copy=False)
|
||||
rec_idx_parts.append(np.full((causes.size,), i, dtype=np.int32))
|
||||
cause_parts.append(causes)
|
||||
dt_parts.append(dts)
|
||||
|
||||
if not rec_idx_parts:
|
||||
return (
|
||||
np.zeros((0,), dtype=np.int32),
|
||||
np.zeros((0,), dtype=np.int64),
|
||||
np.zeros((0,), dtype=np.float32),
|
||||
)
|
||||
|
||||
return (
|
||||
np.concatenate(rec_idx_parts, axis=0),
|
||||
np.concatenate(cause_parts, axis=0),
|
||||
np.concatenate(dt_parts, axis=0),
|
||||
)
|
||||
|
||||
|
||||
# -------------------------
|
||||
# Metrics helpers
|
||||
# -------------------------
|
||||
|
||||
def roc_auc_ovr(y_true: np.ndarray, y_score: np.ndarray) -> float:
|
||||
"""Binary ROC AUC with tie-aware average ranks.
|
||||
|
||||
Returns NaN if y_true has no positives or no negatives.
|
||||
"""
|
||||
y_true = np.asarray(y_true).astype(np.int32)
|
||||
y_score = np.asarray(y_score).astype(np.float64)
|
||||
|
||||
n_pos = int(y_true.sum())
|
||||
n = int(y_true.size)
|
||||
n_neg = n - n_pos
|
||||
if n_pos == 0 or n_neg == 0:
|
||||
return float("nan")
|
||||
|
||||
order = np.argsort(y_score, kind="mergesort")
|
||||
scores_sorted = y_score[order]
|
||||
y_sorted = y_true[order]
|
||||
|
||||
ranks = np.empty(n, dtype=np.float64)
|
||||
i = 0
|
||||
while i < n:
|
||||
j = i + 1
|
||||
while j < n and scores_sorted[j] == scores_sorted[i]:
|
||||
j += 1
|
||||
# average rank for ties, ranks are 1..n
|
||||
avg_rank = 0.5 * (i + 1 + j)
|
||||
ranks[i:j] = avg_rank
|
||||
i = j
|
||||
|
||||
sum_ranks_pos = float((ranks * y_sorted).sum())
|
||||
auc = (sum_ranks_pos - n_pos * (n_pos + 1) / 2.0) / (n_pos * n_neg)
|
||||
return float(auc)
|
||||
|
||||
|
||||
def topk_indices(scores: np.ndarray, k: int) -> np.ndarray:
|
||||
"""Return indices of top-k scores per row (descending)."""
|
||||
if k <= 0:
|
||||
raise ValueError("k must be positive")
|
||||
n, K = scores.shape
|
||||
k = min(k, K)
|
||||
# argpartition gives arbitrary order within topk; sort those by score
|
||||
part = np.argpartition(-scores, kth=k - 1, axis=1)[:, :k]
|
||||
part_scores = np.take_along_axis(scores, part, axis=1)
|
||||
order = np.argsort(-part_scores, axis=1, kind="mergesort")
|
||||
return np.take_along_axis(part, order, axis=1)
|
||||
Reference in New Issue
Block a user