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import os
import unittest
import numpy as np
import hnswlib
class RandomSelfTestCase(unittest.TestCase):
def testRandomSelf(self):
dim = 16
num_elements = 10000
# Generating sample data
data = np.float32(np.random.random((num_elements, dim)))
# Declaring index
p = hnswlib.Index(space='l2', dim=dim) # possible options are l2, cosine or ip
# Initiating index
# max_elements - the maximum number of elements, should be known beforehand
# (probably will be made optional in the future)
#
# ef_construction - controls index search speed/build speed tradeoff
# M - is tightly connected with internal dimensionality of the data
# strongly affects the memory consumption
p.init_index(max_elements=num_elements, ef_construction=100, M=16)
# Controlling the recall by setting ef:
# higher ef leads to better accuracy, but slower search
p.set_ef(10)
p.set_num_threads(4) # by default using all available cores
# We split the data in two batches:
data1 = data[:num_elements // 2]
data2 = data[num_elements // 2:]
print("Adding first batch of %d elements" % (len(data1)))
p.add_items(data1)
# Query the elements for themselves and measure recall:
labels, distances = p.knn_query(data1, k=1)
self.assertAlmostEqual(np.mean(labels.reshape(-1) == np.arange(len(data1))), 1.0, 3)
# Serializing and deleting the index:
index_path = 'first_half.bin'
print("Saving index to '%s'" % index_path)
p.save_index(index_path)
del p
# Re-initiating, loading the index
p = hnswlib.Index(space='l2', dim=dim) # you can change the sa
print("\nLoading index from '%s'\n" % index_path)
p.load_index(index_path)
print("Adding the second batch of %d elements" % (len(data2)))
p.add_items(data2)
# Query the elements for themselves and measure recall:
labels, distances = p.knn_query(data, k=1)
self.assertAlmostEqual(np.mean(labels.reshape(-1) == np.arange(len(data))), 1.0, 3)
os.remove(index_path)
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