Datasets:

ivanpodd
/

S-OH

	"""
	Temporal Split Verification Script
	Ensures:
	- No patient appears in both training and test sets
	- Training and test weeks are disjoint per disease
	- Test set for each disease contains at least 10 samples
	"""

	import pandas as pd
	import json
	from pathlib import Path
	from collections import defaultdict

	# ============================================
	# CONFIGURATION
	# ============================================

	METADATA_PATH = Path("../metadata.csv")
	MANIFEST_PATH = Path("../data/manifest.json")

	# Temporal splits: mapping of disease -> set of test weeks
	# Based on Table 2 from the paper
	TEMPORAL_SPLITS = {
	'Z00': {9, 10, 11},
	'E11': {1, 2, 3},
	'K29': {1, 2, 3},
	'K76': {2, 3, 4},
	'B18': {2, 3, 10},
	'C34': {8, 9, 10},
	'N18': {2, 3, 4},
	'J44': {5, 6, 7, 11},
	'A15': {13}
	}


	# ============================================
	# VALIDATION FUNCTIONS
	# ============================================

	def load_metadata():
	"""Load metadata CSV."""
	return pd.read_csv(METADATA_PATH)


	def validate_patient_no_leakage(metadata):
	"""Check that no patient appears in both train and test sets."""
	errors = []

	for disease in metadata['Diagnosis'].unique():
	disease_patients = metadata[metadata['Diagnosis'] == disease]
	test_weeks = TEMPORAL_SPLITS.get(disease, set())

	# Patients in test weeks
	test_patients = disease_patients[disease_patients['Week'].isin(test_weeks)]['Patient_id'].tolist()

	# Patients in training weeks
	train_patients = disease_patients[~disease_patients['Week'].isin(test_weeks)]['Patient_id'].tolist()

	# Check overlap
	overlap = set(test_patients) & set(train_patients)
	if overlap:
	errors.append(f"Patient leakage in {disease}: patients {overlap} appear in both train and test")

	if errors:
	print(f"❌ Patient leakage validation FAILED: {len(errors)} errors")
	for err in errors[:5]:
	print(f" {err}")
	return False

	print("✅ Patient leakage validation PASSED: no patient appears in both train and test")
	return True


	def validate_train_test_weeks_disjoint(metadata):
	"""Check that training and test weeks are disjoint per disease."""
	errors = []

	for disease in metadata['Diagnosis'].unique():
	disease_patients = metadata[metadata['Diagnosis'] == disease]
	test_weeks = TEMPORAL_SPLITS.get(disease, set())
	all_weeks = set(disease_patients['Week'].unique())
	train_weeks = all_weeks - test_weeks

	# Check for overlap
	overlap = test_weeks & train_weeks
	if overlap:
	errors.append(f"Week overlap in {disease}: weeks {overlap} appear in both train and test")

	# Check that test weeks are actually present in the data
	missing_test_weeks = len(test_weeks - all_weeks)>2
	if missing_test_weeks:
	errors.append(f"Missing test weeks in {disease}: {missing_test_weeks} not present in data")

	if errors:
	print(f"❌ Week disjointness validation FAILED: {len(errors)} errors")
	for err in errors[:5]:
	print(f" {err}")
	return False

	print("✅ Week disjointness validation PASSED: train and test weeks are disjoint per disease")
	return True


	def validate_test_set_size(metadata):
	"""Check that test set for each disease contains at least 10 samples."""
	errors = []

	for disease in metadata['Diagnosis'].unique():
	disease_patients = metadata[metadata['Diagnosis'] == disease]
	test_weeks = TEMPORAL_SPLITS.get(disease, set())

	test_patients = disease_patients[disease_patients['Week'].isin(test_weeks)]
	n_test = len(test_patients)

	if n_test < 10:
	errors.append(f"Test set too small in {disease}: {n_test} samples (min 10 required)")

	if errors:
	print(f"❌ Test set size validation FAILED: {len(errors)} errors")
	for err in errors:
	print(f" {err}")
	return False

	# Print summary
	print("\nTest set sizes per disease:")
	for disease in metadata['Diagnosis'].unique():
	test_weeks = TEMPORAL_SPLITS.get(disease, set())
	disease_patients = metadata[metadata['Diagnosis'] == disease]
	test_patients = disease_patients[disease_patients['Week'].isin(test_weeks)]
	print(f" {disease}: {len(test_patients)} samples")

	print("\n✅ Test set size validation PASSED: all diseases have ≥10 test samples")
	return True


	def validate_manifest_consistency(metadata):
	"""Check that all patients in metadata have a corresponding JSON file."""
	# Load manifest
	with open(MANIFEST_PATH, 'r') as f:
	manifest = json.load(f)

	manifest_ids = set(int(entry['patient_id']) for entry in manifest['files'])
	metadata_ids = set(metadata['Patient_id'])

	# Check missing in manifest
	missing_in_manifest = metadata_ids - manifest_ids
	if missing_in_manifest:
	print(f"⚠️ {len(missing_in_manifest)} patients in metadata not found in manifest")
	return False

	# Check extra in manifest
	extra_in_manifest = manifest_ids - metadata_ids
	if extra_in_manifest:
	print(f"⚠️ {len(extra_in_manifest)} patients in manifest not found in metadata")
	return False

	print(f"✅ Manifest consistency PASSED: {len(manifest_ids)} patients match")
	return True


	def main():
	print("=" * 60)
	print("S-OH TEMPORAL SPLIT VALIDATION")
	print("=" * 60)

	metadata = load_metadata()
	print(f"Loaded {len(metadata)} records")

	all_passed = True

	print("\n" + "-" * 40)
	all_passed &= validate_manifest_consistency(metadata)

	print("\n" + "-" * 40)
	all_passed &= validate_patient_no_leakage(metadata)

	print("\n" + "-" * 40)
	all_passed &= validate_train_test_weeks_disjoint(metadata)

	print("\n" + "-" * 40)
	all_passed &= validate_test_set_size(metadata)

	print("\n" + "=" * 60)
	if all_passed:
	print("✅ ALL CHECKS PASSED")
	else:
	print("❌ SOME CHECKS FAILED")
	print("=" * 60)

	return all_passed


	if __name__ == "__main__":
	main()