Merge pull request #149 from nmslib/develop

Merge changes from develop

Author: yurymalkov

hnswlib/hnswalg.h

@@ -595,6 +595,10 @@ namespace hnswlib {
 
             std::ifstream input(location, std::ios::binary);
 
+            if (!input.is_open())
+                throw std::runtime_error("Cannot open file");
+
+
             // get file size:
             input.seekg(0,input.end);
             std::streampos total_filesize=input.tellg();
@@ -625,16 +629,15 @@ namespace hnswlib {
             fstdistfunc_ = s->get_dist_func();
             dist_func_param_ = s->get_dist_func_param();
 
-            /// Legacy, check that everything is ok
-
-            bool old_index=false;
-
             auto pos=input.tellg();
+            
+            
+            /// Optional - check if index is ok:
+
             input.seekg(cur_element_count * size_data_per_element_,input.cur);
             for (size_t i = 0; i < cur_element_count; i++) {
                 if(input.tellg() < 0 || input.tellg()>=total_filesize){
-                    old_index = true;
-                    break;
+                    throw std::runtime_error("Index seems to be corrupted or unsupported");
                 }
 
                 unsigned int linkListSize;
@@ -644,23 +647,21 @@ namespace hnswlib {
                 }
             }
 
-            // check if file is ok, if not this is either corrupted or old index
+            // throw exception if it either corrupted or old index
             if(input.tellg()!=total_filesize)
-                old_index = true;
+                throw std::runtime_error("Index seems to be corrupted or unsupported");
 
-            if (old_index) {
-                std::cerr << "Warning: loading of old indexes will be deprecated before 2019.\n"
-                          << "Please resave the index in the new format.\n";
-            }
             input.clear();
+
+            /// Optional check end
+
             input.seekg(pos,input.beg);
 
 
             data_level0_memory_ = (char *) malloc(max_elements * size_data_per_element_);
             input.read(data_level0_memory_, cur_element_count * size_data_per_element_);
 
-            if(old_index)
-                input.seekg(((max_elements_-cur_element_count) * size_data_per_element_), input.cur);
+            
 
 
             size_links_per_element_ = maxM_ * sizeof(tableint) + sizeof(linklistsizeint);
@@ -691,6 +692,14 @@ namespace hnswlib {
                     input.read(linkLists_[i], linkListSize);
                 }
             }
+
+            has_deletions_=false;
+
+            for (size_t i = 0; i < cur_element_count; i++) {
+                if(isMarkedDeleted(i))
+                    has_deletions_=true;
+            }
+            
             input.close();
 
             return;

python_bindings/tests/bindings_test_labels.py

@@ -3,10 +3,12 @@
 
 class RandomSelfTestCase(unittest.TestCase):
     def testRandomSelf(self):
+      for idx in range(16):
         print("\n**** Index save-load test ****\n")
         import hnswlib
         import numpy as np
-
+        
+        np.random.seed(idx)
         dim = 16
         num_elements = 10000
 
@@ -95,8 +97,8 @@ def testRandomSelf(self):
             p.mark_deleted(l[0])
         labels2, _ = p.knn_query(data2, k=1)
         items=p.get_items(labels2)
-        diff_with_gt_labels=np.max(np.abs(data2-items))
-        self.assertAlmostEqual(diff_with_gt_labels, 0, delta = 1e-4) # console
+        diff_with_gt_labels=np.mean(np.abs(data2-items))
+        self.assertAlmostEqual(diff_with_gt_labels, 0, delta = 1e-3) # console
 
 
         labels1_after, _ = p.knn_query(data1, k=1)
@@ -106,6 +108,18 @@ def testRandomSelf(self):
                     self.assertTrue(False)
         print("All the data in data1 are removed")
 
+        # checking saving/loading index with elements marked as deleted
+        p.save_index("with_deleted.bin")
+        p = hnswlib.Index(space='l2', dim=dim)
+        p.load_index("with_deleted.bin")
+        p.set_ef(100)
+
+        labels1_after, _ = p.knn_query(data1, k=1)
+        for la in labels1_after:
+            for lb in labels1:
+                if la[0] == lb[0]:
+                    self.assertTrue(False)
+
 
 
 if __name__ == "__main__":

python_bindings/tests/bindings_test_resize.py

@@ -3,11 +3,11 @@
 
 class RandomSelfTestCase(unittest.TestCase):
     def testRandomSelf(self):
-
+      for idx in range(16):
         print("\n**** Index resize test ****\n")
         import hnswlib
         import numpy as np
-
+        np.random.seed(idx)
         dim = 16
         num_elements = 10000
 
@@ -29,9 +29,9 @@ def testRandomSelf(self):
 
         # Controlling the recall by setting ef:
         # higher ef leads to better accuracy, but slower search
-        p.set_ef(100)
+        p.set_ef(20)
 
-        p.set_num_threads(4)  # by default using all available cores
+        p.set_num_threads(idx%8)  # by default using all available cores
 
         # We split the data in two batches:
         data1 = data[:num_elements // 2]
@@ -43,7 +43,7 @@ def testRandomSelf(self):
         # Query the elements for themselves and measure recall:
         labels, distances = p.knn_query(data1, k=1)
 
-        items=p.get_items(labels)
+        items=p.get_items(list(range(len(data1))))
 
         # Check the recall:
         self.assertAlmostEqual(np.mean(labels.reshape(-1) == np.arange(len(data1))),1.0,3)
@@ -62,7 +62,7 @@ def testRandomSelf(self):
 
         # Query the elements for themselves and measure recall:
         labels, distances = p.knn_query(data, k=1)
-        items=p.get_items(labels)
+        items=p.get_items(list(range(num_elements)))
 
         # Check the recall:
         self.assertAlmostEqual(np.mean(labels.reshape(-1) == np.arange(len(data))),1.0,3)