Merge remote-tracking branch 'origin/master' into develop

.travis.yml

@@ -70,7 +70,7 @@ install:
 
 script:
 - $PY --version
-- cd python_bindings && $PY setup.py test && flake8 && cd ..
+- cd python_bindings && $PY setup.py test && cd ..
 - |
   set -e
   if [ "$TRAVIS_OS_NAME" = "linux" ] ; then

data/genunif.py

@@ -26,15 +26,15 @@
 dim  = args['dim']
 
 if args['binary'] and args['gauss']:
-  print "You cannot specify both 'binary' and 'normal'"
+  print("You cannot specify both 'binary' and 'normal'")
   sys.exit(1)
 
 f=open(outf, 'w')
 if args['gauss']:
-  f.write("\n".join(["\t".join([str(random.normalvariate(0,1)) for _ in xrange(dim)]) for _ in xrange(nd+1)]))
+  f.write("\n".join(["\t".join([str(random.normalvariate(0,1)) for _ in range(dim)]) for _ in range(nd+1)]))
 elif args['binary'] :
-  f.write("\n".join(["\t".join([str(random.randint(0,1)) for _ in xrange(dim)]) for _ in xrange(nd+1)]))
+  f.write("\n".join(["\t".join([str(random.randint(0,1)) for _ in range(dim)]) for _ in range(nd+1)]))
 else:
-  f.write("\n".join(["\t".join([str(random.random()) for _ in xrange(dim)]) for _ in xrange(nd+1)]))
+  f.write("\n".join(["\t".join([str(random.random()) for _ in range(dim)]) for _ in range(nd+1)]))
 f.close()
 

python_bindings/notebooks/test_hnsw_recall.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+import logging
+# Uncomment to print logs to the screen
+#logging.basicConfig(level=logging.INFO)
+
+import numpy as np
+import sys 
+import nmslib 
+import time 
+import math 
+
+from sklearn.neighbors import NearestNeighbors
+from sklearn.datasets.samples_generator import make_blobs
+
+def testHnswRecallL2(dataMatrix, queryMatrix, k, M=30, efC=200, efS=1000, numThreads=4):
+  queryQty = queryMatrix.shape[0]
+  indexTimeParams = {'M': M, 'indexThreadQty': numThreads, 'efConstruction': efC, 'post' : 0}
+
+  #Indexing
+  print('Index-time parameters', indexTimeParams)
+  spaceName='l2'
+  index = nmslib.init(method='hnsw', space=spaceName, data_type=nmslib.DataType.DENSE_VECTOR) 
+  index.addDataPointBatch(dataMatrix)  
+
+  start = time.time()
+  index.createIndex(indexTimeParams) 
+  end = time.time() 
+  print('Indexing time = %f' % (end-start))
+
+
+  # Querying
+  start = time.time()
+  nmslibFound = index.knnQueryBatch(queryMatrix, k=k, num_threads=numThreads)
+  end = time.time()
+  print('kNN time total=%f (sec), per query=%f (sec), per query adjusted for thread number=%f (sec)' %
+        (end - start, float(end - start) / queryQty, numThreads * float(end - start) / queryQty))
+
+
+  # Computing gold-standard data 
+  print('Computing gold-standard data')
+
+  start = time.time()
+  sindx = NearestNeighbors(n_neighbors=k, metric='l2', algorithm='brute').fit(dataMatrix)
+  end = time.time()
+
+  print('Brute-force preparation time %f' % (end - start))
+
+  start = time.time() 
+  bruteForceFound = sindx.kneighbors(queryMatrix)
+  end = time.time()
+
+  print('brute-force kNN time total=%f (sec), per query=%f (sec)' % 
+       (end-start, float(end-start)/queryQty) )
+
+  # Setting query-time parameters
+  queryTimeParams = {'efSearch': efS}
+  print('Setting query-time parameters', queryTimeParams)
+  index.setQueryTimeParams(queryTimeParams)
+
+  # Finally computing recall for every i-th neighbor
+  for n in range(k):
+    recall=0.0
+    for i in range(0, queryQty):
+      correctSet = set(bruteForceFound[1][i])
+      retArr = nmslibFound[i][0]
+      retElem = retArr[n] if len(retArr) > n else -1
+
+      recall = recall + int(retElem in correctSet)
+    recall = recall / queryQty
+    print('kNN recall for neighbor %d %f' % (n+1, recall))
+
+
+def testRandomUnif(dataQty, queryQty, efS, dim, k):
+  queryQty = min(dataQty, queryQty)
+  dataMatrix = np.random.randn(dataQty, dim).astype(np.float32)
+  indx = np.random.choice(np.arange(dataQty), size=queryQty, replace=False)
+  queryMatrix = dataMatrix[indx, ].astype(np.float32)
+  testHnswRecallL2(dataMatrix, queryMatrix, k, efS=efS)
+
+
+def testRandomClustered(dataQty, centerQty, queryQty, efS, dim, k):
+  queryQty = min(dataQty, queryQty)
+  dataMatrix, _ = make_blobs(n_samples=dataQty, centers=centerQty, n_features=dim, random_state=0)
+  dataMatrix = dataMatrix.astype(np.float32)
+  indx = np.random.choice(np.arange(dataQty), size=queryQty, replace=False)
+  queryMatrix = dataMatrix[indx, ].astype(np.float32)
+  testHnswRecallL2(dataMatrix, queryMatrix, k, efS=efS)
+
+testRandomClustered(100_000, centerQty=20, queryQty=1000, dim=100, k=10, efS=200)
+testRandomUnif(100_000, 1000, dim=100, k=10, efS=200)
+
+