Turn near-duplicate score test into a property-based test

[elisno]

After updating the near-duplicate scores, a test was added to ensure that near-duplicate examples have worse scores than non-near-duplicates.
RIght now, the test only works on a small, toy dataset. Turning it into a property-based test would be ideal.

So that the test doesn't take too long, it may be a good idea to make a different embedding strategy that:

1. Generates a random array
2. Selects a handful of random examples to have near-duplicates. Append a new example that takes the original example with variable noise.

   # append to existing feature array
   x[sample_ids] + small_random_noise

3. The embedding strategy should always generate "proper" near-duplicates that will get flagged. We don't want to test cases where there are no near-duplicates.

Below is the existing test case for comparison. We want to make sure that the issue scores created by the issue manager don't violate this property.

cleanlab/tests/datalab/issue_manager/test_duplicate.py

Lines 89 to 98 in 23af72a

	def test_scores_of_examples_with_issues_are_smaller_than_those_without(
	self, issue_manager, embeddings
	):
	# TODO: Turn this into a property-based test
	issue_manager.find_issues(features=embeddings["embedding"])
	is_issue = issue_manager.issues["is_near_duplicate_issue"]
	scores = issue_manager.issues["near_duplicate_score"]
	max_issue_score = np.max(scores[is_issue])
	min_non_issue_score = np.min(scores[~is_issue])
	assert max_issue_score < min_non_issue_score

Originally posted by @elisno in #943 (comment)