Task packages for the 10 public-data tasks

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+uci_diabetes/public/train.csv filter=lfs diff=lfs merge=lfs -text
+uci_diabetes_brier/public/train.csv filter=lfs diff=lfs merge=lfs -text
+uci_diabetes_ece/public/train.csv filter=lfs diff=lfs merge=lfs -text

LICENSE

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+Auto-MD-bench task packages: licenses
+
+Each task keeps the license of its source data:
+
+  support                          Vanderbilt Biostatistics data repository terms (use permitted with attribution)
+  metabric                         ODC Open Database License (ODbL) 1.0
+  rotterdam, rotterdam_gbsg        LGPL (>= 2), as distributed in the R package survival
+  nhanes                           U.S. public-use data; NCHS data use restrictions
+  uci_diabetes, uci_diabetes_*     CC BY 4.0
+  physionet2012                    Open Data Commons Attribution License 1.0
+  sepsis, sepsis_*                 CC BY 4.0 (utility_score.py: BSD 2-Clause)
+
+validate.py and the package layout are part of the Auto-MD-bench code, MIT licensed.
+Sources and required attributions are listed in README.md.

README.md

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+---
+pretty_name: Auto-MD-bench
+license: other
+license_name: mixed
+license_link: LICENSE
+language:
+- en
+tags:
+- medical
+- clinical-prediction
+- survival-analysis
+- benchmark
+- agents
+task_categories:
+- tabular-classification
+size_categories:
+- 1M<n<10M
+viewer: false
+---
+
+# Auto-MD-bench: task packages
+
+Auto-MD-bench evaluates autoresearch agents that build prediction models for medical tasks. An agent
+receives a task description and labeled training data, develops a model on its own within a fixed
+time budget, and submits predictions for a hidden test set. Submissions are scored on the task's
+primary metric, on risk calibration and on performance under population shift.
+
+This repository holds the ready-built packages of the 10 benchmark tasks that use public data, plus
+the two objective variants of `uci_diabetes` used in the calibration experiment. The other 5 tasks use
+MIMIC-IV, eICU-CRD and SEER, which cannot be redistributed; build them from the scripts in the code
+repository after obtaining access.
+
+Code, agents, grader and paper results: https://github.com/Edwin0080/Auto-MD-bench
+
+## Tasks in this repository
+
+| Task | Data | Outcome | Validation | Primary metric | Train / test |
+|---|---|---|---|---|---|
+| `support` | SUPPORT | Death | Internal | C<sup>td</sup> | 7,098 / 1,775 |
+| `metabric` | METABRIC | Death | Internal | C<sup>td</sup> | 1,523 / 381 |
+| `rotterdam` | Rotterdam | Recurrence or death | Internal | C<sup>td</sup> | 2,385 / 597 |
+| `rotterdam_gbsg` | Rotterdam → GBSG | Recurrence or death | External (cohort) | C<sup>td</sup> | 2,982 / 686 |
+| `nhanes` | NHANES | Death | Internal | C<sup>td</sup> | 11,639 / 2,910 |
+| `uci_diabetes` | UCI Diabetes, 130 US hospitals | 30-day readmission | Internal | AUROC | 81,347 / 20,419 encounters |
+| `physionet2012` | PhysioNet/CinC 2012, ICU | In-hospital death | Internal | min(Se, PPV) | 4,000 / 8,000 stays |
+| `sepsis` | PhysioNet/CinC 2019, ICU | Sepsis onset | Internal | Utility | 32,268 / 8,068 |
+| `sepsis_new_hospital` | PhysioNet/CinC 2019, ICU | Sepsis onset | External (hospital) | Utility | 20,336 / 4,000 |
+| `sepsis_500_labels` | PhysioNet/CinC 2019, ICU | Sepsis onset | External (hospital) | Utility | 20,336 + 500 / 4,000 |
+
+`uci_diabetes_brier` and `uci_diabetes_ece` have the same data as `uci_diabetes`; only the stated
+objective in `description.md` differs (Brier score; AUROC and ECE).
+
+Not here (build locally): `seer`, `seer_later_period` (SEER Research Data Agreement), `mimiciv`, `eicu`,
+`eicu_new_hospitals` (PhysioNet credentialed access).
+
+## Layout
+
+```
+<task>/
+  public/    what the agent sees: description.md, training data, test features,
+             sample_submission.csv, validate.py (format checker)
+  private/   hidden test labels, used only by the grader
+```
+
+Give an agent only `public/`. The data are the same as in the paper's experiments.
+
+## Usage
+
+```bash
+pip install -U huggingface_hub
+git clone https://github.com/Edwin0080/Auto-MD-bench && cd Auto-MD-bench
+pip install -e .
+hf download Edwin0080/Auto-MD-bench --repo-type dataset --local-dir packages
+python -m automdbench.grade support packages/support/public/sample_submission.csv
+```
+
+See the code repository for running agents and for the scoring protocol.
+
+## Data sources and licenses
+
+Each task keeps the license of its source data. Cite the sources below when you use the tasks.
+
+| Tasks | Source | License or terms |
+|---|---|---|
+| `support` | SUPPORT (Knaus et al., 1995), as processed by DeepSurv (Katzman et al., 2018) from the Vanderbilt Biostatistics data repository | "Permission is granted to anyone wishing to use the data sets"; data obtained from https://hbiostat.org/data courtesy of the Vanderbilt University Department of Biostatistics |
+| `metabric` | METABRIC (Curtis et al., 2012; Pereira et al., 2016; Rueda et al., 2019), as processed by DeepSurv; the same values are in cBioPortal `brca_metabric` | ODC Open Database License (ODbL) 1.0, as distributed by cBioPortal |
+| `rotterdam`, `rotterdam_gbsg` | Rotterdam tumour bank and GBSG trial (Royston and Altman, 2013), as distributed in the R package `survival` | LGPL (≥ 2), the license of the `survival` package |
+| `nhanes` | NHANES 2011–2016 and NCHS public-use linked mortality files | U.S. public-use data; NCHS data use restrictions apply (no attempt to identify individuals) |
+| `uci_diabetes*` | Diabetes 130-US Hospitals for Years 1999–2008, UCI Machine Learning Repository (Strack et al., 2014) | CC BY 4.0 |
+| `physionet2012` | PhysioNet/CinC Challenge 2012 (Silva et al., 2012; Goldberger et al., 2000) | Open Data Commons Attribution License 1.0 |
+| `sepsis*` | PhysioNet/CinC Challenge 2019 (Reyna et al., 2020; Goldberger et al., 2000) | CC BY 4.0 |
+
+The `sepsis*` packages include `utility_score.py`, the official PhysioNet/CinC Challenge 2019 scoring
+functions (BSD 2-Clause). The `validate.py` format checkers are part of the benchmark code (MIT).

metabric/private/test_labels.csv

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metabric/public/description.md

@@ -0,0 +1,50 @@
+# Task: Survival prediction on the METABRIC breast-cancer cohort
+
+You are building a survival model for breast-cancer patients (METABRIC
+cohort, 1,523 training patients). Each row is one patient with 9 numeric
+covariates (`x0`..`x8`: four gene-expression markers and five clinical
+indicators; the exact dictionary is not provided) and a **right-censored**
+survival outcome:
+
+- `duration`: follow-up time (months); `event`: 1 = death observed, 0 =
+  censored (alive at last contact — true survival time only known to exceed
+  `duration`).
+
+## Files
+
+| file | contents |
+|---|---|
+| `train.csv` | 1,523 patients WITH labels — all the labeled data you get |
+| `test_features.csv` | 381 held-out patients, features only |
+| `time_grid.csv` | evaluation times (quantile-spaced): submission column ↔ time |
+| `sample_submission.csv` | valid null example: train-set Kaplan-Meier for every patient |
+| `validate.py` | format checker — run `python validate.py submission.csv` |
+
+## What to submit
+
+`./submission.csv` with columns `patient_id, t_000, t_001, ...` (every column
+in `time_grid.csv`, in order): column `t_k` = your predicted probability that
+the patient survives beyond the mapped time (values in [0,1], non-increasing
+along each row), one row per test patient.
+
+## Scoring (computed on hidden test labels from your file alone)
+
+- **`concordance_td`** (Antolini) — primary, higher is better
+
+## Environment
+
+One machine: 8 CPU threads (4 physical cores), 16 GB of memory, no GPU. The
+memory limit is enforced: a process that exceeds it is killed. `python` (3.11)
+with numpy, pandas, pyarrow, scipy, scikit-learn, lightgbm, xgboost, torch
+(CPU), torchtuples, pycox, lifelines, scikit-survival and matplotlib (the
+example datasets bundled with lifelines, scikit-survival and pycox have been
+removed). No network access; additional packages cannot be installed. Use only
+the files provided here.
+
+## Rules
+
+- Work entirely inside this directory; do not read or write files outside it.
+- Do not attempt to locate the hidden test labels.
+- The run has a fixed wall-clock budget of 12 hours (720 minutes). The
+  `submission.csv` on disk when the budget ends is what gets graded; an
+  invalid or missing submission is a failed run.

metabric/public/test_features.csv

@@ -0,0 +1,382 @@
+patient_id,x0,x1,x2,x3,x4,x5,x6,x7,x8
+m0000,5.574418,5.3433843,13.691147,5.6124806,1.0,0.0,0.0,1.0,80.17
+m0001,5.5656075,7.874823,10.599864,5.788013,1.0,1.0,0.0,1.0,74.76
+m0002,6.30144,6.90969,10.918006,5.689066,0.0,1.0,0.0,1.0,51.87
+m0003,5.8922563,6.4251266,11.480591,5.5511627,1.0,1.0,0.0,1.0,55.62
+m0004,5.8366413,5.714075,14.302764,6.0770955,1.0,1.0,1.0,0.0,49.57
+m0005,6.0911365,7.450691,11.289801,5.929166,1.0,1.0,0.0,1.0,60.77
+m0006,5.701148,5.3374133,10.301611,6.131167,1.0,1.0,0.0,1.0,68.82
+m0007,6.846967,5.291367,10.298811,6.0909443,0.0,1.0,1.0,0.0,36.93
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metabric/public/time_grid.csv

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+column,time
+t_000,0.0
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metabric/public/validate.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+"""Submission format checker (no labels involved).
+
+Usage:  python validate.py [submission.csv]
+
+Checks column layout, patient coverage, value range and monotonicity, then
+appends one JSON line per call to ./validate_log.jsonl. Passing this check
+means your file is GRADABLE — it says nothing about how good the scores are.
+"""
+
+import hashlib
+import json
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+HERE = Path(__file__).resolve().parent
+TOL = 1e-6
+
+
+def sha256(path):
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 16), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def check(path: Path):
+    issues = []
+    try:
+        sub = pd.read_csv(path)
+    except Exception as e:  # noqa: BLE001
+        return [f"cannot read CSV: {e}"]
+    grid_cols = pd.read_csv(HERE / "time_grid.csv")["column"].tolist()
+    expected_cols = ["patient_id"] + grid_cols
+    if list(sub.columns) != expected_cols:
+        return [
+            f"columns mismatch: expected patient_id + the {len(grid_cols)} columns "
+            f"named in time_grid.csv, got {len(sub.columns)} columns"
+        ]
+    expected_ids = set(pd.read_csv(HERE / "test_features.csv")["patient_id"].tolist())
+    ids = sub["patient_id"].tolist()
+    if len(ids) != len(set(ids)):
+        issues.append("duplicate patient_id rows")
+    if set(ids) != expected_ids:
+        issues.append(
+            f"patient_id set mismatch: {len(expected_ids - set(ids))} missing, "
+            f"{len(set(ids) - expected_ids)} unexpected"
+        )
+    vals = sub[expected_cols[1:]].to_numpy(dtype=float)
+    if not np.isfinite(vals).all():
+        issues.append("non-finite values (NaN/inf)")
+    else:
+        if vals.min() < -TOL or vals.max() > 1 + TOL:
+            issues.append(f"values outside [0,1]: min={vals.min():.4g} max={vals.max():.4g}")
+        n_viol = int((np.diff(vals, axis=1) > TOL).sum())
+        if n_viol:
+            issues.append(f"not non-increasing: {n_viol} increasing steps")
+    return issues
+
+
+def main():
+    target = Path(sys.argv[1]) if len(sys.argv) > 1 else HERE / "submission.csv"
+    if not target.exists():
+        print(f"FAIL: {target} not found")
+        sys.exit(1)
+    issues = check(target)
+    entry = {
+        "ts": time.strftime("%Y-%m-%dT%H:%M:%S"),
+        "file": str(target.name),
+        "sha256": sha256(target),
+        "valid": not issues,
+        "issues": issues,
+    }
+    with open(HERE / "validate_log.jsonl", "a") as f:
+        f.write(json.dumps(entry) + "\n")
+    if issues:
+        print("FAIL:")
+        for i in issues:
+            print(f"  - {i}")
+        sys.exit(1)
+    print("OK: submission format is valid and gradable")
+
+
+if __name__ == "__main__":
+    main()

nhanes/private/test_labels.csv

@@ -0,0 +1,2911 @@
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nhanes/public/description.md

@@ -0,0 +1,76 @@
+# Task: All-cause mortality after a national health examination (NHANES)
+
+You are building a **survival model** for all-cause mortality in US adults,
+using data from the National Health and Nutrition Examination Survey (NHANES)
+linked to the National Death Index. Participants were examined between 2011 and
+2016 and followed through the end of 2019, so follow-up runs from about 3 to 8
+years and most people are still alive at the end — they are **right-censored**,
+not event-free.
+
+`duration` is months from the examination to death or to the end of follow-up;
+`event` is 1 if the participant died, 0 if they were censored.
+
+## This is survey data, not a hospital cohort
+
+NHANES is a **complex probability sample** of the US population, not a
+convenience sample. Three design columns ship with the data:
+
+| column | meaning |
+|---|---|
+| `survey_weight` | examination weight — how many people in the population this participant represents |
+| `survey_stratum` | sampling stratum |
+| `survey_psu` | primary sampling unit (cluster) within the stratum |
+
+Selection probabilities are deliberately unequal: some groups were oversampled
+by design.
+
+## Features
+
+Demographics (`age`, `sex` (1 = male), `race_eth`, `education`,
+`income_poverty_ratio`), body measures (`bmi`, `waist_cm`), blood pressure
+(`sbp`, `dbp` — averaged over repeated readings), lipids
+(`total_cholesterol`, `hdl_cholesterol`), `hba1c`, blood counts (`wbc`,
+`hemoglobin`, `platelets`), biochemistry (`creatinine`, `albumin`, `glucose`,
+`alk_phos`, `ast`), and `ever_smoked_100` (smoked 100+ cigarettes in life).
+`cycle` is the survey cycle the participant came from.
+
+Missing values are frequent and vary by component — not everyone got every lab.
+
+## Files
+
+| file | contents |
+|---|---|
+| `train.csv` | visible participants WITH `duration` / `event` |
+| `test_features.csv` | held-out participants, features only |
+| `time_grid.csv` | evaluation times (quantile-spaced): submission column ↔ time in months |
+| `sample_submission.csv` | valid null example: the training-set Kaplan-Meier curve for everyone |
+| `validate.py` | format checker — `python validate.py submission.csv` |
+
+## What to submit
+
+`./submission.csv` with columns `patient_id, t_000, t_001, ...` (every column
+in `time_grid.csv`, in order): `t_k` = predicted probability of surviving
+beyond the mapped time ([0,1], non-increasing along each row), one row per
+participant in `test_features.csv`.
+
+## Scoring (hidden labels, from your file alone)
+
+- **`concordance_td`** (Antolini) — primary, higher better
+
+## Environment
+
+One machine: 8 CPU threads (4 physical cores), 16 GB of memory, no GPU. The
+memory limit is enforced: a process that exceeds it is killed. `python` (3.11)
+with numpy, pandas, pyarrow, scipy, scikit-learn, lightgbm, xgboost, torch
+(CPU), torchtuples, pycox, lifelines, scikit-survival and matplotlib (the
+example datasets bundled with lifelines, scikit-survival and pycox have been
+removed). No network access; additional packages cannot be installed. Use only
+the files provided here.
+
+## Rules
+
+- Work entirely inside this directory; do not read or write files outside it.
+- Do not attempt to locate the hidden test labels.
+- The run has a fixed wall-clock budget of 12 hours (720 minutes). The
+  `submission.csv` on disk when the budget ends is what gets graded; an
+  invalid or missing submission is a failed run.

nhanes/public/time_grid.csv

@@ -0,0 +1,87 @@
+column,time
+t_000,0.0
+t_001,1.0
+t_002,2.0
+t_003,3.0
+t_004,5.0
+t_005,6.0
+t_006,7.0
+t_007,8.0
+t_008,9.0
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nhanes/public/validate.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+"""Submission format checker (no labels involved).
+
+Usage:  python validate.py [submission.csv]
+
+Checks column layout, patient coverage, value range and monotonicity, then
+appends one JSON line per call to ./validate_log.jsonl. Passing this check
+means your file is GRADABLE — it says nothing about how good the scores are.
+"""
+
+import hashlib
+import json
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+HERE = Path(__file__).resolve().parent
+TOL = 1e-6
+
+
+def sha256(path):
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 16), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def check(path: Path):
+    issues = []
+    try:
+        sub = pd.read_csv(path)
+    except Exception as e:  # noqa: BLE001
+        return [f"cannot read CSV: {e}"]
+    grid_cols = pd.read_csv(HERE / "time_grid.csv")["column"].tolist()
+    expected_cols = ["patient_id"] + grid_cols
+    if list(sub.columns) != expected_cols:
+        return [
+            f"columns mismatch: expected patient_id + the {len(grid_cols)} columns "
+            f"named in time_grid.csv, got {len(sub.columns)} columns"
+        ]
+    expected_ids = set(pd.read_csv(HERE / "test_features.csv")["patient_id"].tolist())
+    ids = sub["patient_id"].tolist()
+    if len(ids) != len(set(ids)):
+        issues.append("duplicate patient_id rows")
+    if set(ids) != expected_ids:
+        issues.append(
+            f"patient_id set mismatch: {len(expected_ids - set(ids))} missing, "
+            f"{len(set(ids) - expected_ids)} unexpected"
+        )
+    vals = sub[expected_cols[1:]].to_numpy(dtype=float)
+    if not np.isfinite(vals).all():
+        issues.append("non-finite values (NaN/inf)")
+    else:
+        if vals.min() < -TOL or vals.max() > 1 + TOL:
+            issues.append(f"values outside [0,1]: min={vals.min():.4g} max={vals.max():.4g}")
+        n_viol = int((np.diff(vals, axis=1) > TOL).sum())
+        if n_viol:
+            issues.append(f"not non-increasing: {n_viol} increasing steps")
+    return issues
+
+
+def main():
+    target = Path(sys.argv[1]) if len(sys.argv) > 1 else HERE / "submission.csv"
+    if not target.exists():
+        print(f"FAIL: {target} not found")
+        sys.exit(1)
+    issues = check(target)
+    entry = {
+        "ts": time.strftime("%Y-%m-%dT%H:%M:%S"),
+        "file": str(target.name),
+        "sha256": sha256(target),
+        "valid": not issues,
+        "issues": issues,
+    }
+    with open(HERE / "validate_log.jsonl", "a") as f:
+        f.write(json.dumps(entry) + "\n")
+    if issues:
+        print("FAIL:")
+        for i in issues:
+            print(f"  - {i}")
+        sys.exit(1)
+    print("OK: submission format is valid and gradable")
+
+
+if __name__ == "__main__":
+    main()

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physionet2012/public/description.md

@@ -0,0 +1,61 @@
+# Task: ICU in-hospital mortality from the first 48 hours (PhysioNet 2012)
+
+Predict **in-hospital death** for ICU stays from the first 48 hours of
+routinely collected data. This is a RETROSPECTIVE stay-level prediction: you
+see the full 48-hour window, so whole-window aggregates are fine (no
+real-time constraint).
+
+## Data format (long time series)
+
+`train_series.parquet` / `test_features.parquet`, one row per observation:
+
+- `record_id` — the ICU stay you predict for
+- `minutes` — minutes since ICU admission (0..2880)
+- `parameter` — what was measured (about 37 time-varying signals: vitals,
+  labs, urine output; plus the general descriptors `Age`, `Gender`, `Height`,
+  `ICUType`, `Weight` recorded at minute 0)
+- `value` — the measurement (missing static descriptors are NaN)
+
+Measurements are irregular and sparse — most parameters are observed only a
+few times per stay. `train_labels.csv` maps `record_id` to the label
+(1 = died in hospital, ~14% of stays).
+
+## Files
+
+| file | contents |
+|---|---|
+| `train_series.parquet` + `train_labels.csv` | 4,000 labeled ICU stays |
+| `test_features.parquet` | 8,000 held-out stays (the challenge's two test cohorts, merged) |
+| `sample_submission.csv` | valid null: training prevalence, never predicting death |
+| `validate.py` | format checker — `python validate.py submission.csv` |
+
+## What to submit
+
+`./submission.csv` with exactly: `record_id, PredictedProbability,
+PredictedLabel` — probability of in-hospital death in [0,1] and your binary
+prediction in {0,1} (the official score uses the binary column), one row per
+test stay.
+
+## Scoring (hidden labels, from your file alone)
+
+- **`score1_min_se_ppv`** — primary; the challenge's official event-1 score:
+  min(sensitivity, positive predictive value) of your binary predictions.
+  Never predicting death scores 0.
+
+## Environment
+
+One machine: 8 CPU threads (4 physical cores), 16 GB of memory, no GPU. The
+memory limit is enforced: a process that exceeds it is killed. `python` (3.11)
+with numpy, pandas, pyarrow, scipy, scikit-learn, lightgbm, xgboost, torch
+(CPU), torchtuples, pycox, lifelines, scikit-survival and matplotlib (the
+example datasets bundled with lifelines, scikit-survival and pycox have been
+removed). No network access; additional packages cannot be installed. Use only
+the files provided here.
+
+## Rules
+
+- Work entirely inside this directory; do not read or write files outside it.
+- Do not attempt to locate the hidden test labels.
+- The run has a fixed wall-clock budget of 12 hours (720 minutes). The
+  `submission.csv` on disk when the budget ends is what gets graded; an
+  invalid or missing submission is a failed run.

physionet2012/public/test_features.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:065432b57b208ef207ce349873eacc613af955e0a8c492330f2b568b71fb166d
+size 9969946

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physionet2012/public/train_series.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7100fa2ca3b2bbdf52f87393b8fdca40450d7129e0bbc760af1b92286d0d4c21
+size 4976927

physionet2012/public/validate.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python
+"""Submission format checker (no labels involved). Built per-task from a
+template: record_id / True are substituted at capsule build time.
+
+Usage:  python validate.py [submission.csv]
+"""
+
+import hashlib
+import json
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+HERE = Path(__file__).resolve().parent
+ID_COL = "record_id"
+NEED_LABEL = True
+
+
+def sha256(path):
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 16), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def check(path: Path):
+    try:
+        sub = pd.read_csv(path)
+    except Exception as e:  # noqa: BLE001
+        return [f"cannot read CSV: {e}"]
+    cols = [ID_COL, "PredictedProbability"] + (["PredictedLabel"] if NEED_LABEL else [])
+    if list(sub.columns) != cols:
+        return [f"columns mismatch: expected exactly {cols}"]
+    issues = []
+    if sub[ID_COL].duplicated().any():
+        issues.append(f"duplicate {ID_COL} rows")
+    tf = HERE / "test_features.csv"
+    if not tf.exists():
+        tf = HERE / "test_features.parquet"
+        exp = pd.read_parquet(tf, columns=[ID_COL])[ID_COL]
+    else:
+        exp = pd.read_csv(tf, usecols=[ID_COL])[ID_COL]
+    a = np.sort(sub[ID_COL].to_numpy())
+    b = np.sort(exp.unique())
+    if len(a) != len(b) or not (a == b).all():
+        issues.append(f"{ID_COL} set mismatch ({len(a)} vs {len(b)} expected)")
+    probs = sub["PredictedProbability"].to_numpy(dtype=float)
+    if not np.isfinite(probs).all():
+        issues.append("non-finite probabilities")
+    elif probs.min() < 0 or probs.max() > 1:
+        issues.append(f"probabilities outside [0,1]: min={probs.min():.4g} "
+                      f"max={probs.max():.4g}")
+    if NEED_LABEL and not sub["PredictedLabel"].isin([0, 1]).all():
+        issues.append("PredictedLabel must be 0 or 1")
+    return issues
+
+
+def main():
+    target = Path(sys.argv[1]) if len(sys.argv) > 1 else HERE / "submission.csv"
+    if not target.exists():
+        print(f"FAIL: {target} not found")
+        sys.exit(1)
+    issues = check(target)
+    entry = {"ts": time.strftime("%Y-%m-%dT%H:%M:%S"), "file": str(target.name),
+             "sha256": sha256(target), "valid": not issues, "issues": issues}
+    with open(HERE / "validate_log.jsonl", "a") as f:
+        f.write(json.dumps(entry) + "\n")
+    if issues:
+        print("FAIL:")
+        for i in issues:
+            print(f"  - {i}")
+        sys.exit(1)
+    print("OK: submission format is valid and gradable")
+
+
+if __name__ == "__main__":
+    main()

rotterdam/private/test_labels.csv

@@ -0,0 +1,598 @@
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rotterdam/public/description.md

@@ -0,0 +1,51 @@
+# Task: Recurrence-free survival on the Rotterdam breast-cancer cohort
+
+You are building a survival model for **recurrence-free survival** (time to
+recurrence or death, whichever first) in breast-cancer patients from the
+Rotterdam tumor-bank cohort. One row per patient, 8 clinical covariates:
+
+`age` (years), `meno` (menopausal status 0/1), `sizecat` (tumor size: 1 =
+≤20mm, 2 = 20–50mm, 3 = >50mm), `grade` (differentiation grade), `nodes`
+(positive lymph nodes), `pgr` (progesterone receptor, fmol/l), `er`
+(estrogen receptor, fmol/l), `hormon` (hormonal therapy 0/1) — plus the
+**right-censored** outcome `duration` (days) / `event` (1 = recurrence or
+death observed; 0 = censored).
+
+## Files
+
+| file | contents |
+|---|---|
+| `train.csv` | 2,385 patients WITH labels |
+| `test_features.csv` | 597 held-out patients, features only |
+| `time_grid.csv` | evaluation times (quantile-spaced): submission column ↔ time |
+| `sample_submission.csv` | valid null example: train-set Kaplan-Meier for every patient |
+| `validate.py` | format checker — `python validate.py submission.csv` |
+
+## What to submit
+
+`./submission.csv` with columns `patient_id, t_000, t_001, ...` (every column
+in `time_grid.csv`, in order): `t_k` = predicted probability of surviving
+event-free beyond the mapped time ([0,1], non-increasing per row), one row
+per test patient.
+
+## Scoring (hidden test labels, from your file alone)
+
+- **`concordance_td`** (Antolini) — primary, higher better
+
+## Environment
+
+One machine: 8 CPU threads (4 physical cores), 16 GB of memory, no GPU. The
+memory limit is enforced: a process that exceeds it is killed. `python` (3.11)
+with numpy, pandas, pyarrow, scipy, scikit-learn, lightgbm, xgboost, torch
+(CPU), torchtuples, pycox, lifelines, scikit-survival and matplotlib (the
+example datasets bundled with lifelines, scikit-survival and pycox have been
+removed). No network access; additional packages cannot be installed. Use only
+the files provided here.
+
+## Rules
+
+- Work entirely inside this directory; do not read or write files outside it.
+- Do not attempt to locate the hidden test labels.
+- The run has a fixed wall-clock budget of 12 hours (720 minutes). The
+  `submission.csv` on disk when the budget ends is what gets graded; an
+  invalid or missing submission is a failed run.

rotterdam/public/test_features.csv

@@ -0,0 +1,598 @@
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rotterdam/public/validate.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+"""Submission format checker (no labels involved).
+
+Usage:  python validate.py [submission.csv]
+
+Checks column layout, patient coverage, value range and monotonicity, then
+appends one JSON line per call to ./validate_log.jsonl. Passing this check
+means your file is GRADABLE — it says nothing about how good the scores are.
+"""
+
+import hashlib
+import json
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+HERE = Path(__file__).resolve().parent
+TOL = 1e-6
+
+
+def sha256(path):
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 16), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def check(path: Path):
+    issues = []
+    try:
+        sub = pd.read_csv(path)
+    except Exception as e:  # noqa: BLE001
+        return [f"cannot read CSV: {e}"]
+    grid_cols = pd.read_csv(HERE / "time_grid.csv")["column"].tolist()
+    expected_cols = ["patient_id"] + grid_cols
+    if list(sub.columns) != expected_cols:
+        return [
+            f"columns mismatch: expected patient_id + the {len(grid_cols)} columns "
+            f"named in time_grid.csv, got {len(sub.columns)} columns"
+        ]
+    expected_ids = set(pd.read_csv(HERE / "test_features.csv")["patient_id"].tolist())
+    ids = sub["patient_id"].tolist()
+    if len(ids) != len(set(ids)):
+        issues.append("duplicate patient_id rows")
+    if set(ids) != expected_ids:
+        issues.append(
+            f"patient_id set mismatch: {len(expected_ids - set(ids))} missing, "
+            f"{len(set(ids) - expected_ids)} unexpected"
+        )
+    vals = sub[expected_cols[1:]].to_numpy(dtype=float)
+    if not np.isfinite(vals).all():
+        issues.append("non-finite values (NaN/inf)")
+    else:
+        if vals.min() < -TOL or vals.max() > 1 + TOL:
+            issues.append(f"values outside [0,1]: min={vals.min():.4g} max={vals.max():.4g}")
+        n_viol = int((np.diff(vals, axis=1) > TOL).sum())
+        if n_viol:
+            issues.append(f"not non-increasing: {n_viol} increasing steps")
+    return issues
+
+
+def main():
+    target = Path(sys.argv[1]) if len(sys.argv) > 1 else HERE / "submission.csv"
+    if not target.exists():
+        print(f"FAIL: {target} not found")
+        sys.exit(1)
+    issues = check(target)
+    entry = {
+        "ts": time.strftime("%Y-%m-%dT%H:%M:%S"),
+        "file": str(target.name),
+        "sha256": sha256(target),
+        "valid": not issues,
+        "issues": issues,
+    }
+    with open(HERE / "validate_log.jsonl", "a") as f:
+        f.write(json.dumps(entry) + "\n")
+    if issues:
+        print("FAIL:")
+        for i in issues:
+            print(f"  - {i}")
+        sys.exit(1)
+    print("OK: submission format is valid and gradable")
+
+
+if __name__ == "__main__":
+    main()

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@@ -0,0 +1,687 @@
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rotterdam_gbsg/public/description.md

@@ -0,0 +1,50 @@
+# Task: Breast-cancer survival — EXTERNAL VALIDATION on a different cohort
+
+You are building a **recurrence-free survival** model (time to recurrence or
+death). **Important: your training data are the Rotterdam tumor-bank cohort
+(a Dutch registry), but your model will be evaluated on the GBSG cohort — a
+German randomized-trial population.** Case mix, treatment patterns, and
+follow-up differ between the two cohorts.
+
+One row per patient, 8 harmonized covariates: `age`, `meno`, `sizecat`
+(1 = ≤20mm, 2 = 20–50mm, 3 = >50mm), `grade`, `nodes`, `pgr`, `er`,
+`hormon` — plus right-censored `duration` (days) / `event`.
+
+## Files
+
+| file | contents |
+|---|---|
+| `train.csv` | 2,982 Rotterdam patients WITH labels |
+| `test_features.csv` | 686 GBSG patients, features only |
+| `time_grid.csv` | evaluation times (quantile-spaced): submission column ↔ time |
+| `sample_submission.csv` | valid null example: train-set Kaplan-Meier for every patient |
+| `validate.py` | format checker — `python validate.py submission.csv` |
+
+## What to submit
+
+`./submission.csv` with columns `patient_id, t_000, t_001, ...` (every column
+in `time_grid.csv`, in order): `t_k` = predicted probability of surviving
+event-free beyond the mapped time ([0,1], non-increasing per row), one row
+per test patient.
+
+## Scoring (hidden GBSG labels, from your file alone)
+
+- **`concordance_td`** (Antolini) — primary, higher better
+
+## Environment
+
+One machine: 8 CPU threads (4 physical cores), 16 GB of memory, no GPU. The
+memory limit is enforced: a process that exceeds it is killed. `python` (3.11)
+with numpy, pandas, pyarrow, scipy, scikit-learn, lightgbm, xgboost, torch
+(CPU), torchtuples, pycox, lifelines, scikit-survival and matplotlib (the
+example datasets bundled with lifelines, scikit-survival and pycox have been
+removed). No network access; additional packages cannot be installed. Use only
+the files provided here.
+
+## Rules
+
+- Work entirely inside this directory; do not read or write files outside it.
+- Do not attempt to locate the hidden test labels.
+- The run has a fixed wall-clock budget of 12 hours (720 minutes). The
+  `submission.csv` on disk when the budget ends is what gets graded; an
+  invalid or missing submission is a failed run.

rotterdam_gbsg/public/test_features.csv

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rotterdam_gbsg/public/time_grid.csv

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rotterdam_gbsg/public/validate.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python
+"""Submission format checker (no labels involved).
+
+Usage:  python validate.py [submission.csv]
+
+Checks column layout, patient coverage, value range and monotonicity, then
+appends one JSON line per call to ./validate_log.jsonl. Passing this check
+means your file is GRADABLE — it says nothing about how good the scores are.
+"""
+
+import hashlib
+import json
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+HERE = Path(__file__).resolve().parent
+TOL = 1e-6
+
+
+def sha256(path):
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 16), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def check(path: Path):
+    issues = []
+    try:
+        sub = pd.read_csv(path)
+    except Exception as e:  # noqa: BLE001
+        return [f"cannot read CSV: {e}"]
+    grid_cols = pd.read_csv(HERE / "time_grid.csv")["column"].tolist()
+    expected_cols = ["patient_id"] + grid_cols
+    if list(sub.columns) != expected_cols:
+        return [
+            f"columns mismatch: expected patient_id + the {len(grid_cols)} columns "
+            f"named in time_grid.csv, got {len(sub.columns)} columns"
+        ]
+    expected_ids = set(pd.read_csv(HERE / "test_features.csv")["patient_id"].tolist())
+    ids = sub["patient_id"].tolist()
+    if len(ids) != len(set(ids)):
+        issues.append("duplicate patient_id rows")
+    if set(ids) != expected_ids:
+        issues.append(
+            f"patient_id set mismatch: {len(expected_ids - set(ids))} missing, "
+            f"{len(set(ids) - expected_ids)} unexpected"
+        )
+    vals = sub[expected_cols[1:]].to_numpy(dtype=float)
+    if not np.isfinite(vals).all():
+        issues.append("non-finite values (NaN/inf)")
+    else:
+        if vals.min() < -TOL or vals.max() > 1 + TOL:
+            issues.append(f"values outside [0,1]: min={vals.min():.4g} max={vals.max():.4g}")
+        n_viol = int((np.diff(vals, axis=1) > TOL).sum())
+        if n_viol:
+            issues.append(f"not non-increasing: {n_viol} increasing steps")
+    return issues
+
+
+def main():
+    target = Path(sys.argv[1]) if len(sys.argv) > 1 else HERE / "submission.csv"
+    if not target.exists():
+        print(f"FAIL: {target} not found")
+        sys.exit(1)
+    issues = check(target)
+    entry = {
+        "ts": time.strftime("%Y-%m-%dT%H:%M:%S"),
+        "file": str(target.name),
+        "sha256": sha256(target),
+        "valid": not issues,
+        "issues": issues,
+    }
+    with open(HERE / "validate_log.jsonl", "a") as f:
+        f.write(json.dumps(entry) + "\n")
+    if issues:
+        print("FAIL:")
+        for i in issues:
+            print(f"  - {i}")
+        sys.exit(1)
+    print("OK: submission format is valid and gradable")
+
+
+if __name__ == "__main__":
+    main()

sepsis/private/test_labels.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5bc090de7b84f8dd923049691d7e7421e2d86734a8c28ccdfe1619642f103097
+size 149514

sepsis/public/description.md

@@ -0,0 +1,71 @@
+# Task: Early prediction of sepsis (PhysioNet/CinC Challenge 2019 data)
+
+You are building an **hourly early-warning model for sepsis** in ICU patients.
+The data are hourly records from two hospital systems (column `hospital`: A/B):
+8 vital signs, 26 laboratory values, and 6 demographics per hour (40 feature
+columns total, heavy missingness — labs are measured infrequently), plus:
+
+- `patient`: patient identifier (each patient is one ICU stay)
+- `ICULOS`: hour counter since ICU admission (1, 2, 3, ...)
+- `SepsisLabel` (train only): becomes 1 starting **6 hours before** clinical
+  sepsis onset and stays 1 afterwards; always 0 for non-septic patients.
+  About 7% of patients ever become septic.
+
+## The real-time rule (critical)
+
+This is an early-warning task: your prediction for patient p at hour t must be
+computable **using only that patient's rows with ICULOS ≤ t** (plus anything
+learned from the training set). Later hours of the same test patient must not
+inform earlier predictions. Carrying values forward and backward-looking
+rolling statistics are allowed; centered windows and whole-stay aggregates are
+not.
+
+## Files in this directory
+
+| file | contents |
+|---|---|
+| `train.parquet` | ~32k patients (~1.2M rows) WITH `SepsisLabel` |
+| `test_features.parquet` | ~8k held-out patients, features only |
+| `sample_submission.csv` | all-zero predictions — a valid "never alarm" null (scores exactly 0) |
+| `utility_score.py` | the official challenge scoring code, with a `score_cohort(labels_df, pred_df)` helper |
+| `validate.py` | format checker — run `python validate.py submission.csv` |
+
+## What to submit
+
+Write `./submission.csv` with exactly these columns:
+
+```
+patient, ICULOS, PredictedProbability, PredictedLabel
+```
+
+one row for every (patient, ICULOS) pair in `test_features.parquet`:
+`PredictedProbability` in [0,1], `PredictedLabel` in {0,1} (your alarm
+decision; the utility score is computed from these binary labels).
+
+## How you are scored
+
+- **`utility_norm`** — primary. The challenge's official clinical utility,
+  normalized so that never alarming = 0 and clinically optimal alarming = 1.
+  Per patient-hour: a true alarm earns up to +1 (maximum when raised 6h before
+  onset, decaying to 0 at 12h early or 3h late), a false alarm costs −0.05,
+  a missed septic hour after onset costs up to −2. Negative totals are
+  possible. Exact definition: `utility_score.py`.
+
+## Environment
+
+One machine: 8 CPU threads (4 physical cores), 16 GB of memory, no GPU. The
+memory limit is enforced: a process that exceeds it is killed. `python` (3.11)
+with numpy, pandas, pyarrow, scipy, scikit-learn, lightgbm, xgboost, torch
+(CPU), torchtuples, pycox, lifelines, scikit-survival and matplotlib (the
+example datasets bundled with lifelines, scikit-survival and pycox have been
+removed). No network access; additional packages cannot be installed. Use only
+the files provided here.
+
+## Rules
+
+- Work entirely inside this directory; do not read or write files outside it.
+- Do not attempt to locate the hidden test labels.
+- Honor the real-time rule above; code and submissions are reviewed for it.
+- The run has a fixed wall-clock budget of 12 hours (720 minutes). The
+  `submission.csv` on disk when the budget ends is what gets graded; an
+  invalid or missing submission is a failed run.

sepsis/public/test_features.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:57320355beae54ac501f1ff33c7f896a0a3472f563fa66d5754b263f9a9bb407
+size 3444754

sepsis/public/train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:932e22bff22309bb85d766923156e8bab70d6683911c292508ae4792a686f6d7
+size 13546160

sepsis/public/utility_score.py

@@ -0,0 +1,315 @@
+"""Official PhysioNet/CinC Challenge 2019 scoring functions, vendored VERBATIM.
+
+Source: https://github.com/physionetchallenges/evaluation-2019
+        evaluate_sepsis_score.py (BSD 2-Clause, PhysioNet Challenge organizers)
+Only the three pure scoring functions are kept (the psv-directory I/O wrapper
+is not needed); nothing inside the function bodies is modified. Any change to
+scoring semantics must happen in sepsis_scoring.py, never here.
+"""
+
+import warnings
+
+import numpy as np
+
+
+def compute_auc(labels, predictions, check_errors=True):
+    # Check inputs for errors.
+    if check_errors:
+        if len(predictions) != len(labels):
+            raise Exception('Numbers of predictions and labels must be the same.')
+
+        for label in labels:
+            if not label in (0, 1):
+                raise Exception('Labels must satisfy label == 0 or label == 1.')
+
+        for prediction in predictions:
+            if not 0 <= prediction <= 1:
+                warnings.warn('Predictions do not satisfy 0 <= prediction <= 1.')
+
+    # Find prediction thresholds.
+    thresholds = np.unique(predictions)[::-1]
+    if thresholds[0] != 1:
+        thresholds = np.insert(thresholds, 0, 1)
+    if thresholds[-1] == 0:
+        thresholds = thresholds[:-1]
+
+    n = len(labels)
+    m = len(thresholds)
+
+    # Populate contingency table across prediction thresholds.
+    tp = np.zeros(m)
+    fp = np.zeros(m)
+    fn = np.zeros(m)
+    tn = np.zeros(m)
+
+    # Find indices that sort the predicted probabilities from largest to
+    # smallest.
+    idx = np.argsort(predictions)[::-1]
+
+    i = 0
+    for j in range(m):
+        # Initialize contingency table for j-th prediction threshold.
+        if j == 0:
+            tp[j] = 0
+            fp[j] = 0
+            fn[j] = np.sum(labels)
+            tn[j] = n - fn[j]
+        else:
+            tp[j] = tp[j - 1]
+            fp[j] = fp[j - 1]
+            fn[j] = fn[j - 1]
+            tn[j] = tn[j - 1]
+
+        # Update contingency table for i-th largest predicted probability.
+        while i < n and predictions[idx[i]] >= thresholds[j]:
+            if labels[idx[i]]:
+                tp[j] += 1
+                fn[j] -= 1
+            else:
+                fp[j] += 1
+                tn[j] -= 1
+            i += 1
+
+    # Summarize contingency table.
+    tpr = np.zeros(m)
+    tnr = np.zeros(m)
+    ppv = np.zeros(m)
+    npv = np.zeros(m)
+
+    for j in range(m):
+        if tp[j] + fn[j]:
+            tpr[j] = tp[j] / (tp[j] + fn[j])
+        else:
+            tpr[j] = 1
+        if fp[j] + tn[j]:
+            tnr[j] = tn[j] / (fp[j] + tn[j])
+        else:
+            tnr[j] = 1
+        if tp[j] + fp[j]:
+            ppv[j] = tp[j] / (tp[j] + fp[j])
+        else:
+            ppv[j] = 1
+        if fn[j] + tn[j]:
+            npv[j] = tn[j] / (fn[j] + tn[j])
+        else:
+            npv[j] = 1
+
+    # Compute AUROC as the area under a piecewise linear function with TPR /
+    # sensitivity (x-axis) and TNR / specificity (y-axis) and AUPRC as the area
+    # under a piecewise constant with TPR / recall (x-axis) and PPV / precision
+    # (y-axis).
+    auroc = 0
+    auprc = 0
+    for j in range(m-1):
+        auroc += 0.5 * (tpr[j + 1] - tpr[j]) * (tnr[j + 1] + tnr[j])
+        auprc += (tpr[j + 1] - tpr[j]) * ppv[j + 1]
+
+    return auroc, auprc
+
+
+def compute_accuracy_f_measure(labels, predictions, check_errors=True):
+    # Check inputs for errors.
+    if check_errors:
+        if len(predictions) != len(labels):
+            raise Exception('Numbers of predictions and labels must be the same.')
+
+        for label in labels:
+            if not label in (0, 1):
+                raise Exception('Labels must satisfy label == 0 or label == 1.')
+
+        for prediction in predictions:
+            if not prediction in (0, 1):
+                raise Exception('Predictions must satisfy prediction == 0 or prediction == 1.')
+
+    # Populate contingency table.
+    n = len(labels)
+    tp = 0
+    fp = 0
+    fn = 0
+    tn = 0
+
+    for i in range(n):
+        if labels[i] and predictions[i]:
+            tp += 1
+        elif not labels[i] and predictions[i]:
+            fp += 1
+        elif labels[i] and not predictions[i]:
+            fn += 1
+        elif not labels[i] and not predictions[i]:
+            tn += 1
+
+    # Summarize contingency table.
+    if tp + fp + fn + tn:
+        accuracy = float(tp + tn) / float(tp + fp + fn + tn)
+    else:
+        accuracy = 1.0
+
+    if 2 * tp + fp + fn:
+        f_measure = float(2 * tp) / float(2 * tp + fp + fn)
+    else:
+        f_measure = 1.0
+
+    return accuracy, f_measure
+
+
+def compute_prediction_utility(labels, predictions, dt_early=-12, dt_optimal=-6, dt_late=3.0, max_u_tp=1, min_u_fn=-2, u_fp=-0.05, u_tn=0, check_errors=True):
+    # Check inputs for errors.
+    if check_errors:
+        if len(predictions) != len(labels):
+            raise Exception('Numbers of predictions and labels must be the same.')
+
+        for label in labels:
+            if not label in (0, 1):
+                raise Exception('Labels must satisfy label == 0 or label == 1.')
+
+        for prediction in predictions:
+            if not prediction in (0, 1):
+                raise Exception('Predictions must satisfy prediction == 0 or prediction == 1.')
+
+        if dt_early >= dt_optimal:
+            raise Exception('The earliest beneficial time for predictions must be before the optimal time.')
+
+        if dt_optimal >= dt_late:
+            raise Exception('The optimal time for predictions must be before the latest beneficial time.')
+
+    # Does the patient eventually have sepsis?
+    if np.any(labels):
+        is_septic = True
+        t_sepsis = np.argmax(labels) - dt_optimal
+    else:
+        is_septic = False
+        t_sepsis = float('inf')
+
+    n = len(labels)
+
+    # Define slopes and intercept points for utility functions of the form
+    # u = m * t + b.
+    m_1 = float(max_u_tp) / float(dt_optimal - dt_early)
+    b_1 = -m_1 * dt_early
+    m_2 = float(-max_u_tp) / float(dt_late - dt_optimal)
+    b_2 = -m_2 * dt_late
+    m_3 = float(min_u_fn) / float(dt_late - dt_optimal)
+    b_3 = -m_3 * dt_optimal
+
+    # Compare predicted and true conditions.
+    u = np.zeros(n)
+    for t in range(n):
+        if t <= t_sepsis + dt_late:
+            # TP
+            if is_septic and predictions[t]:
+                if t <= t_sepsis + dt_optimal:
+                    u[t] = max(m_1 * (t - t_sepsis) + b_1, u_fp)
+                elif t <= t_sepsis + dt_late:
+                    u[t] = m_2 * (t - t_sepsis) + b_2
+            # FP
+            elif not is_septic and predictions[t]:
+                u[t] = u_fp
+            # FN
+            elif is_septic and not predictions[t]:
+                if t <= t_sepsis + dt_optimal:
+                    u[t] = 0
+                elif t <= t_sepsis + dt_late:
+                    u[t] = m_3 * (t - t_sepsis) + b_3
+            # TN
+            elif not is_septic and not predictions[t]:
+                u[t] = u_tn
+
+    # Find total utility for patient.
+    return np.sum(u)
+
+
+# ======================================================================
+"""Cohort-level sepsis scoring on DataFrames, mirroring the official
+evaluate_sepsis_score() cohort loop (lines that compute best/inaction
+predictions and the normalization) exactly, with the official per-patient
+functions imported verbatim from sepsis_official.
+
+Normalization identity (from the official code): all-zero predictions score
+exactly 0; predicting exactly the optimal window for every septic patient
+scores exactly 1. test_scoring_identities() checks both.
+"""
+
+import numpy as np
+import pandas as pd
+
+# (official functions are defined above in this file)
+
+DT_EARLY, DT_OPTIMAL, DT_LATE = -12, -6, 3
+MAX_U_TP, MIN_U_FN, U_FP, U_TN = 1, -2, -0.05, 0
+
+
+def score_cohort(labels_df: pd.DataFrame, pred_df: pd.DataFrame) -> dict:
+    """labels_df: patient, ICULOS, SepsisLabel. pred_df: patient, ICULOS,
+    PredictedProbability, PredictedLabel. Must cover exactly the same
+    (patient, ICULOS) pairs; rows are aligned here by sorting both the same way.
+    """
+    lab = labels_df.sort_values(["patient", "ICULOS"])
+    pred = pred_df.sort_values(["patient", "ICULOS"])
+    if len(lab) != len(pred):
+        raise ValueError("labels and predictions have different row counts")
+    if not (lab["patient"].to_numpy() == pred["patient"].to_numpy()).all():
+        raise ValueError("patient alignment mismatch")
+    if not (lab["ICULOS"].to_numpy() == pred["ICULOS"].to_numpy()).all():
+        raise ValueError("ICULOS alignment mismatch")
+
+    all_labels = lab["SepsisLabel"].to_numpy(dtype=int)
+    all_probs = pred["PredictedProbability"].to_numpy(dtype=float)
+
+    observed_u, best_u, inaction_u = [], [], []
+    for _, g in pd.concat(
+        [lab.reset_index(drop=True),
+         pred.reset_index(drop=True)[["PredictedProbability", "PredictedLabel"]]],
+        axis=1,
+    ).groupby("patient", sort=False):
+        labels = g["SepsisLabel"].to_numpy(dtype=int)
+        predictions = g["PredictedLabel"].to_numpy(dtype=int)
+        num_rows = len(labels)
+
+        best_predictions = np.zeros(num_rows)
+        if np.any(labels):
+            t_sepsis = np.argmax(labels) - DT_OPTIMAL
+            best_predictions[max(0, t_sepsis + DT_EARLY):
+                             min(t_sepsis + DT_LATE + 1, num_rows)] = 1
+        inaction_predictions = np.zeros(num_rows)
+
+        args = (DT_EARLY, DT_OPTIMAL, DT_LATE, MAX_U_TP, MIN_U_FN, U_FP, U_TN)
+        observed_u.append(compute_prediction_utility(labels, predictions, *args,
+                                                     check_errors=False))
+        best_u.append(compute_prediction_utility(labels, best_predictions, *args,
+                                                 check_errors=False))
+        inaction_u.append(compute_prediction_utility(labels, inaction_predictions,
+                                                     *args, check_errors=False))
+
+    obs, best, inaction = map(np.sum, (observed_u, best_u, inaction_u))
+    utility_norm = float((obs - inaction) / (best - inaction))
+    auroc, auprc = compute_auc(all_labels, all_probs, check_errors=False)
+    return {
+        "utility_norm": utility_norm,
+        "auroc": float(auroc),
+        "auprc": float(auprc),
+    }
+
+
+def test_scoring_identities(labels_df: pd.DataFrame) -> None:
+    """Sanity checks required before trusting any real score."""
+    zero = labels_df[["patient", "ICULOS"]].copy()
+    zero["PredictedProbability"] = 0.0
+    zero["PredictedLabel"] = 0
+    s0 = score_cohort(labels_df, zero)
+    assert abs(s0["utility_norm"] - 0.0) < 1e-12, s0
+
+    optimal = []
+    for pid, g in labels_df.sort_values(["patient", "ICULOS"]).groupby("patient",
+                                                                       sort=False):
+        labels = g["SepsisLabel"].to_numpy(dtype=int)
+        p = np.zeros(len(labels))
+        if np.any(labels):
+            t_sepsis = np.argmax(labels) - DT_OPTIMAL
+            p[max(0, t_sepsis + DT_EARLY): min(t_sepsis + DT_LATE + 1, len(labels))] = 1
+        d = g[["patient", "ICULOS"]].copy()
+        d["PredictedProbability"] = p
+        d["PredictedLabel"] = p.astype(int)
+        optimal.append(d)
+    s1 = score_cohort(labels_df, pd.concat(optimal))
+    assert abs(s1["utility_norm"] - 1.0) < 1e-12, s1
+    print("scoring identities ok: inaction=0, optimal=1")

sepsis/public/validate.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+"""Submission format checker for the sepsis task (no labels involved).
+
+Usage:  python validate.py [submission.csv]
+
+Checks column layout, exact (patient, ICULOS) coverage against
+test_features.parquet, probability range, and binary labels. Appends one JSON
+line per call to ./validate_log.jsonl. Passing means your file is GRADABLE —
+it says nothing about the scores.
+"""
+
+import hashlib
+import json
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+HERE = Path(__file__).resolve().parent
+REQUIRED_COLS = ["patient", "ICULOS", "PredictedProbability", "PredictedLabel"]
+
+
+def sha256(path):
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 16), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def check(path: Path):
+    try:
+        sub = pd.read_csv(path)
+    except Exception as e:  # noqa: BLE001
+        return [f"cannot read CSV: {e}"]
+    if list(sub.columns) != REQUIRED_COLS:
+        return [f"columns mismatch: expected exactly {REQUIRED_COLS}"]
+    issues = []
+    if sub[["patient", "ICULOS"]].duplicated().any():
+        issues.append("duplicate (patient, ICULOS) rows")
+    exp = pd.read_parquet(HERE / "test_features.parquet",
+                          columns=["patient", "ICULOS"])
+    a = sub.sort_values(["patient", "ICULOS"])
+    b = exp.sort_values(["patient", "ICULOS"])
+    if len(a) != len(b) or not (
+        (a["patient"].to_numpy() == b["patient"].to_numpy()).all()
+        and (a["ICULOS"].to_numpy() == b["ICULOS"].to_numpy()).all()
+    ):
+        issues.append(f"(patient, ICULOS) pairs must exactly match "
+                      f"test_features.parquet ({len(a)} vs {len(b)} rows)")
+    probs = sub["PredictedProbability"].to_numpy(dtype=float)
+    if not np.isfinite(probs).all():
+        issues.append("non-finite probabilities")
+    elif probs.min() < 0 or probs.max() > 1:
+        issues.append(f"probabilities outside [0,1]: min={probs.min():.4g} "
+                      f"max={probs.max():.4g}")
+    if not sub["PredictedLabel"].isin([0, 1]).all():
+        issues.append("PredictedLabel must be 0 or 1")
+    return issues
+
+
+def main():
+    target = Path(sys.argv[1]) if len(sys.argv) > 1 else HERE / "submission.csv"
+    if not target.exists():
+        print(f"FAIL: {target} not found")
+        sys.exit(1)
+    issues = check(target)
+    entry = {
+        "ts": time.strftime("%Y-%m-%dT%H:%M:%S"),
+        "file": str(target.name),
+        "sha256": sha256(target),
+        "valid": not issues,
+        "issues": issues,
+    }
+    with open(HERE / "validate_log.jsonl", "a") as f:
+        f.write(json.dumps(entry) + "\n")
+    if issues:
+        print("FAIL:")
+        for i in issues:
+            print(f"  - {i}")
+        sys.exit(1)
+    print("OK: submission format is valid and gradable")
+
+
+if __name__ == "__main__":
+    main()

sepsis_500_labels/private/test_labels.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37aec6683522026fd1126a806a992501132f6995d7f53fe802ebf7b5d941e15a
+size 72324

sepsis_500_labels/public/description.md

@@ -0,0 +1,63 @@
+# Task: Early prediction of sepsis — FEW-SHOT adaptation to a new hospital
+
+You are building an **hourly early-warning model for sepsis** in ICU patients.
+**Your training data are all of hospital system A plus only 500 labeled
+patients from hospital system B — and your model will be evaluated on other
+hospital-B patients.** Sites differ in measurement practice and patient mix.
+
+The data are hourly ICU records (column `hospital`: A/B): 8 vital signs, 26
+laboratory values, 6 demographics per hour (40 feature columns, heavy
+missingness), plus:
+
+- `patient`, `ICULOS` (hour counter), and `SepsisLabel` (train only):
+  becomes 1 starting **6 hours before** sepsis onset; ~7% of patients ever
+  become septic.
+
+## The real-time rule (critical)
+
+Predictions for patient p at hour t may use only that patient's rows with
+ICULOS ≤ t (plus anything learned from training). Carrying values forward
+and backward-looking rolling statistics are allowed; centered windows and
+whole-stay aggregates are not.
+
+## Files
+
+| file | contents |
+|---|---|
+| `train.parquet` | 20,336 hospital-A patients + 500 hospital-B patients, WITH `SepsisLabel` |
+| `test_features.parquet` | 4,000 other hospital-B patients, features only |
+| `sample_submission.csv` | all-zero predictions — valid "never alarm" null (scores exactly 0) |
+| `utility_score.py` | the official challenge scoring code + `score_cohort` helper |
+| `validate.py` | format checker — `python validate.py submission.csv` |
+
+## What to submit
+
+`./submission.csv` with exactly: `patient, ICULOS, PredictedProbability,
+PredictedLabel` — one row for every (patient, ICULOS) pair in
+`test_features.parquet`; the utility score is computed from the binary
+alarms.
+
+## Scoring
+
+- **`utility_norm`** — primary; official clinical utility on the hospital-B
+  test patients (never alarm = 0, clinically optimal = 1; false alarm −0.05,
+  late/missed alarms down to −2). Exact definition: `utility_score.py`.
+
+## Environment
+
+One machine: 8 CPU threads (4 physical cores), 16 GB of memory, no GPU. The
+memory limit is enforced: a process that exceeds it is killed. `python` (3.11)
+with numpy, pandas, pyarrow, scipy, scikit-learn, lightgbm, xgboost, torch
+(CPU), torchtuples, pycox, lifelines, scikit-survival and matplotlib (the
+example datasets bundled with lifelines, scikit-survival and pycox have been
+removed). No network access; additional packages cannot be installed. Use only
+the files provided here.
+
+## Rules
+
+- Work entirely inside this directory; do not read or write files outside it.
+- Do not attempt to locate the hidden test labels.
+- Honor the real-time rule above; code and submissions are reviewed for it.
+- The run has a fixed wall-clock budget of 12 hours (720 minutes). The
+  `submission.csv` on disk when the budget ends is what gets graded; an
+  invalid or missing submission is a failed run.

sepsis_500_labels/public/test_features.parquet

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+oid sha256:d8585a15296fb8a9f814d3fd0bc3496e25a690c9e95ad7686710e056d146c381
+size 1571318

sepsis_500_labels/public/train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b0a00db80664635a23ffbb1434ccf43cecf21b3d045e1070e5af1ad92a4e856
+size 9166637