[Code scan] Do not require save_path for in-memory target scaling

[njzjz]

This issue is a result of a Codex global repository scan.

Summary

TargetScaler.fit always removes, creates, and writes target_scaler.ss under dump_dir after fitting. DataHub accepts save_path=None, so direct regression or multilabel regression use with target_normalize enabled can fail even though the fitted scaler could be kept in memory.

Code references

unimol_tools/unimol_tools/data/datascaler.py

Lines 120 to 125 in 4596596

	try:
	os.remove(os.path.join(dump_dir, 'target_scaler.ss'))
	except:
	pass
	os.makedirs(dump_dir, exist_ok=True)
	joblib.dump(self.scaler, os.path.join(dump_dir, 'target_scaler.ss'))

unimol_tools/unimol_tools/data/datahub.py

Lines 58 to 65 in 4596596

	self.data['target_scaler'] = TargetScaler(
	self.ss_method, self.task, self.save_path
	)
	if self.task == 'regression':
	target = np.array(self.data['raw_target']).reshape(-1, 1).astype(np.float32)
	if self.is_train:
	self.data['target_scaler'].fit(target, self.save_path)
	self.data['target'] = self.data['target_scaler'].transform(target)

Impact

Library users who construct DataHub directly, or future call paths that do not provide save_path, can crash during scaler fitting despite not needing scaler persistence on disk.

Suggested fix

Only persist the scaler when dump_dir is not None. If dump_dir is None, keep self.scaler in memory and skip file IO.