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Diffstat (limited to 'examples/redis-unstable/modules/vector-sets/tests/deletion.py')
| -rw-r--r-- | examples/redis-unstable/modules/vector-sets/tests/deletion.py | 173 |
1 files changed, 0 insertions, 173 deletions
diff --git a/examples/redis-unstable/modules/vector-sets/tests/deletion.py b/examples/redis-unstable/modules/vector-sets/tests/deletion.py deleted file mode 100644 index cb91959..0000000 --- a/examples/redis-unstable/modules/vector-sets/tests/deletion.py +++ /dev/null @@ -1,173 +0,0 @@ -from test import TestCase, fill_redis_with_vectors, generate_random_vector -import random - -""" -A note about this test: -It was experimentally tried to modify hnsw.c in order to -avoid calling hnsw_reconnect_nodes(). In this case, the test -fails very often with EF set to 250, while it hardly -fails at all with the same parameters if hnsw_reconnect_nodes() -is called. - -Note that for the nature of the test (it is very strict) it can -still fail from time to time, without this signaling any -actual bug. -""" - -class VREM(TestCase): - def getname(self): - return "Deletion and graph state after deletion" - - def estimated_runtime(self): - return 2.0 - - def format_neighbors_with_scores(self, links_result, old_links=None, items_to_remove=None): - """Format neighbors with their similarity scores and status indicators""" - if not links_result: - return "No neighbors" - - output = [] - for level, neighbors in enumerate(links_result): - level_num = len(links_result) - level - 1 - output.append(f"Level {level_num}:") - - # Get neighbors and scores - neighbors_with_scores = [] - for i in range(0, len(neighbors), 2): - neighbor = neighbors[i].decode() if isinstance(neighbors[i], bytes) else neighbors[i] - score = float(neighbors[i+1]) if i+1 < len(neighbors) else None - status = "" - - # For old links, mark deleted ones - if items_to_remove and neighbor in items_to_remove: - status = " [lost]" - # For new links, mark newly added ones - elif old_links is not None: - # Check if this neighbor was in the old links at this level - was_present = False - if old_links and level < len(old_links): - old_neighbors = [n.decode() if isinstance(n, bytes) else n - for n in old_links[level]] - was_present = neighbor in old_neighbors - if not was_present: - status = " [gained]" - - if score is not None: - neighbors_with_scores.append(f"{len(neighbors_with_scores)+1}. {neighbor} ({score:.6f}){status}") - else: - neighbors_with_scores.append(f"{len(neighbors_with_scores)+1}. {neighbor}{status}") - - output.extend([" " + n for n in neighbors_with_scores]) - return "\n".join(output) - - def test(self): - # 1. Fill server with random elements - dim = 128 - count = 5000 - data = fill_redis_with_vectors(self.redis, self.test_key, count, dim) - - # 2. Do VSIM to get 200 items - query_vec = generate_random_vector(dim) - results = self.redis.execute_command('VSIM', self.test_key, 'VALUES', dim, - *[str(x) for x in query_vec], - 'COUNT', 200, 'WITHSCORES') - - # Convert results to list of (item, score) pairs, sorted by score - items = [] - for i in range(0, len(results), 2): - item = results[i].decode() - score = float(results[i+1]) - items.append((item, score)) - items.sort(key=lambda x: x[1], reverse=True) # Sort by similarity - - # Store the graph structure for all items before deletion - neighbors_before = {} - for item, _ in items: - links = self.redis.execute_command('VLINKS', self.test_key, item, 'WITHSCORES') - if links: # Some items might not have links - neighbors_before[item] = links - - # 3. Remove 100 random items - items_to_remove = set(item for item, _ in random.sample(items, 100)) - # Keep track of top 10 non-removed items - top_remaining = [] - for item, score in items: - if item not in items_to_remove: - top_remaining.append((item, score)) - if len(top_remaining) == 10: - break - - # Remove the items - for item in items_to_remove: - result = self.redis.execute_command('VREM', self.test_key, item) - assert result == 1, f"VREM failed to remove {item}" - - # 4. Do VSIM again with same vector - new_results = self.redis.execute_command('VSIM', self.test_key, 'VALUES', dim, - *[str(x) for x in query_vec], - 'COUNT', 200, 'WITHSCORES', - 'EF', 500) - - # Convert new results to dict of item -> score - new_scores = {} - for i in range(0, len(new_results), 2): - item = new_results[i].decode() - score = float(new_results[i+1]) - new_scores[item] = score - - failure = False - failed_item = None - failed_reason = None - # 5. Verify all top 10 non-removed items are still found with similar scores - for item, old_score in top_remaining: - if item not in new_scores: - failure = True - failed_item = item - failed_reason = "missing" - break - new_score = new_scores[item] - if abs(new_score - old_score) >= 0.01: - failure = True - failed_item = item - failed_reason = f"score changed: {old_score:.6f} -> {new_score:.6f}" - break - - if failure: - print("\nTest failed!") - print(f"Problem with item: {failed_item} ({failed_reason})") - - print("\nOriginal neighbors (with similarity scores):") - if failed_item in neighbors_before: - print(self.format_neighbors_with_scores( - neighbors_before[failed_item], - items_to_remove=items_to_remove)) - else: - print("No neighbors found in original graph") - - print("\nCurrent neighbors (with similarity scores):") - current_links = self.redis.execute_command('VLINKS', self.test_key, - failed_item, 'WITHSCORES') - if current_links: - print(self.format_neighbors_with_scores( - current_links, - old_links=neighbors_before.get(failed_item))) - else: - print("No neighbors in current graph") - - print("\nOriginal results (top 20):") - for item, score in items[:20]: - deleted = "[deleted]" if item in items_to_remove else "" - print(f"{item}: {score:.6f} {deleted}") - - print("\nNew results after removal (top 20):") - new_items = [] - for i in range(0, len(new_results), 2): - item = new_results[i].decode() - score = float(new_results[i+1]) - new_items.append((item, score)) - new_items.sort(key=lambda x: x[1], reverse=True) - for item, score in new_items[:20]: - print(f"{item}: {score:.6f}") - - raise AssertionError(f"Test failed: Problem with item {failed_item} ({failed_reason}). *** IMPORTANT *** This test may fail from time to time without indicating that there is a bug. However normally it should pass. The fact is that it's a quite extreme test where we destroy 50% of nodes of top results and still expect perfect recall, with vectors that are very hostile because of the distribution used.") - |