Update python recall test (#415)

.github/workflows/build.yml

@@ -19,7 +19,7 @@ jobs:
         run: python -m pip install .
 
       - name: Test
-        run: python -m unittest discover --start-directory python_bindings/tests --pattern "*_test*.py"
+        run: python -m unittest discover -v --start-directory python_bindings/tests --pattern "*_test*.py"
 
   test_cpp:
     runs-on: ${{matrix.os}}

python_bindings/tests/bindings_test_recall.py

@@ -1,88 +1,100 @@
+import os
 import hnswlib
 import numpy as np
-
-dim = 32
-num_elements = 100000
-k = 10
-nun_queries = 10
-
-# Generating sample data
-data = np.float32(np.random.random((num_elements, dim)))
-
-# Declaring index
-hnsw_index = hnswlib.Index(space='l2', dim=dim)  # possible options are l2, cosine or ip
-bf_index = hnswlib.BFIndex(space='l2', dim=dim)
-
-# Initing both hnsw and brute force indices
-# max_elements - the maximum number of elements (capacity). Will throw an exception if exceeded
-# during insertion of an element.
-# The capacity can be increased by saving/loading the index, see below.
-#
-# hnsw construction params:
-# ef_construction - controls index search speed/build speed tradeoff
-#
-# M - is tightly connected with internal dimensionality of the data. Strongly affects the memory consumption (~M)
-# Higher M leads to higher accuracy/run_time at fixed ef/efConstruction
-
-hnsw_index.init_index(max_elements=num_elements, ef_construction=200, M=16)
-bf_index.init_index(max_elements=num_elements)
-
-# Controlling the recall for hnsw by setting ef:
-# higher ef leads to better accuracy, but slower search
-hnsw_index.set_ef(200)
-
-# Set number of threads used during batch search/construction in hnsw
-# By default using all available cores
-hnsw_index.set_num_threads(1)
-
-print("Adding batch of %d elements" % (len(data)))
-hnsw_index.add_items(data)
-bf_index.add_items(data)
-
-print("Indices built")
-
-# Generating query data
-query_data = np.float32(np.random.random((nun_queries, dim)))
-
-# Query the elements and measure recall:
-labels_hnsw, distances_hnsw = hnsw_index.knn_query(query_data, k)
-labels_bf, distances_bf = bf_index.knn_query(query_data, k)
-
-# Measure recall
-correct = 0
-for i in range(nun_queries):
-    for label in labels_hnsw[i]:
-        for correct_label in labels_bf[i]:
-            if label == correct_label:
-                correct += 1
-                break
-
-print("recall is :", float(correct)/(k*nun_queries))
-
-# test serializing  the brute force index
-index_path = 'bf_index.bin'
-print("Saving index to '%s'" % index_path)
-bf_index.save_index(index_path)
-del bf_index
-
-# Re-initiating, loading the index
-bf_index = hnswlib.BFIndex(space='l2', dim=dim)
-
-print("\nLoading index from '%s'\n" % index_path)
-bf_index.load_index(index_path)
-
-# Query the brute force index again to verify that we get the same results
-labels_bf, distances_bf = bf_index.knn_query(query_data, k)
-
-# Measure recall
-correct = 0
-for i in range(nun_queries):
-    for label in labels_hnsw[i]:
-        for correct_label in labels_bf[i]:
-            if label == correct_label:
-                correct += 1
-                break
-
-print("recall after reloading is :", float(correct)/(k*nun_queries))
-
-
+import unittest
+
+
+class RandomSelfTestCase(unittest.TestCase):
+    def testRandomSelf(self):
+        dim = 32
+        num_elements = 100000
+        k = 10
+        num_queries = 20
+
+        recall_threshold = 0.95
+
+        # Generating sample data
+        data = np.float32(np.random.random((num_elements, dim)))
+
+        # Declaring index
+        hnsw_index = hnswlib.Index(space='l2', dim=dim)  # possible options are l2, cosine or ip
+        bf_index = hnswlib.BFIndex(space='l2', dim=dim)
+
+        # Initing both hnsw and brute force indices
+        # max_elements - the maximum number of elements (capacity). Will throw an exception if exceeded
+        # during insertion of an element.
+        # The capacity can be increased by saving/loading the index, see below.
+        #
+        # hnsw construction params:
+        # ef_construction - controls index search speed/build speed tradeoff
+        #
+        # M - is tightly connected with internal dimensionality of the data. Strongly affects the memory consumption (~M)
+        # Higher M leads to higher accuracy/run_time at fixed ef/efConstruction
+
+        hnsw_index.init_index(max_elements=num_elements, ef_construction=200, M=16)
+        bf_index.init_index(max_elements=num_elements)
+
+        # Controlling the recall for hnsw by setting ef:
+        # higher ef leads to better accuracy, but slower search
+        hnsw_index.set_ef(200)
+
+        # Set number of threads used during batch search/construction in hnsw
+        # By default using all available cores
+        hnsw_index.set_num_threads(1)
+
+        print("Adding batch of %d elements" % (len(data)))
+        hnsw_index.add_items(data)
+        bf_index.add_items(data)
+
+        print("Indices built")
+
+        # Generating query data
+        query_data = np.float32(np.random.random((num_queries, dim)))
+
+        # Query the elements and measure recall:
+        labels_hnsw, distances_hnsw = hnsw_index.knn_query(query_data, k)
+        labels_bf, distances_bf = bf_index.knn_query(query_data, k)
+
+        # Measure recall
+        correct = 0
+        for i in range(num_queries):
+            for label in labels_hnsw[i]:
+                for correct_label in labels_bf[i]:
+                    if label == correct_label:
+                        correct += 1
+                        break
+
+        recall_before = float(correct) / (k*num_queries)
+        print("recall is :", recall_before)
+        self.assertGreater(recall_before, recall_threshold)
+
+        # test serializing  the brute force index
+        index_path = 'bf_index.bin'
+        print("Saving index to '%s'" % index_path)
+        bf_index.save_index(index_path)
+        del bf_index
+
+        # Re-initiating, loading the index
+        bf_index = hnswlib.BFIndex(space='l2', dim=dim)
+
+        print("\nLoading index from '%s'\n" % index_path)
+        bf_index.load_index(index_path)
+
+        # Query the brute force index again to verify that we get the same results
+        labels_bf, distances_bf = bf_index.knn_query(query_data, k)
+
+        # Measure recall
+        correct = 0
+        for i in range(num_queries):
+            for label in labels_hnsw[i]:
+                for correct_label in labels_bf[i]:
+                    if label == correct_label:
+                        correct += 1
+                        break
+
+        recall_after = float(correct) / (k*num_queries)
+        print("recall after reloading is :", recall_after)
+
+        self.assertEqual(recall_before, recall_after)
+
+        os.remove(index_path)