Header-only C++ HNSW implementation with python bindings. Paper code for the HNSW 200M SIFT experiment
NEW: Added support for cosine similarity and inner product distances
Part of the nmslib project https://github.com/nmslib/nmslib
Offers less memory footprint and faster builds compared to current nmslib's version.
| Distance | parameter | Equation |
|---|---|---|
| Squared L2 | 'l2' | d = sum((Ai-Bi)^2) |
| Inner product | 'ip' | d = 1.0 - sum(Ai*Bi)) |
| Cosine similarity | 'cosine' | d = 1.0 - sum(Ai*Bi) / sqrt(sum(Ai*Ai) * sum(Bi*Bi)) |
Note that inner product is not a metric. An element can be closer to some other element than to itself.
For other spaces use the main library https://github.com/nmslib/nmslib
import hnswlib
import numpy as np
dim = 128
num_elements = 10000
# Generating sample data
data = np.float32(np.random.random((num_elements, dim)))
data_labels = np.arange(num_elements)
# Declaring index
p = hnswlib.Index(space = 'l2', dim = dim) # possible options are l2, cosine or ip
# Initing index - the maximum number of elements should be known beforehand
p.init_index(max_elements = num_elements, ef_construction = 200, M = 16)
# Element insertion (can be called several times):
p.add_items(data, data_labels)
# Controlling the recall by setting ef:
p.set_ef(50) # ef should always be > k
# Query dataset, k - number of closest elements (returns 2 numpy arrays)
labels, distances = p.knn_query(data, k = 1)An example with updates after serialization/deserialization:
import hnswlib
import numpy as np
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
# Initing 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
# stronlgy 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)
print("Recall for the first batch:", np.mean(labels.reshape(-1) == np.arange(len(data1))), "\n")
# Serializing and deleting the index:
index_path='first_half.bin'
print("Saving index to '%s'" % index_path)
p.save_index("first_half.bin")
del p
# Reiniting, loading the index
p = hnswlib.Index(space='l2', dim=dim) # you can change the sa
print("\nLoading index from 'first_half.bin'\n")
p.load_index("first_half.bin")
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)
print("Recall for two batches:", np.mean(labels.reshape(-1) == np.arange(len(data))), "\n")pip3 install pybind11 numpy setuptools
cd python_bindings
python3 setup.py installTo download and extract the bigann dataset:
python3 download_bigann.pyTo compile:
cmake .
make allTo run the test on 200M SIFT subset:
./mainThe size of the bigann subset (in millions) is controlled by the variable subset_size_milllions hardcoded in sift_1b.cpp.
- Visual search engine for 1M amazon products (MXNet + HNSW): website, code, demo by @ThomasDelteil
Malkov, Yu A., and D. A. Yashunin. "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs." arXiv preprint arXiv:1603.09320 (2016). https://arxiv.org/abs/1603.09320