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faiss计算余弦距离

2021-10-28 18:01:10 作者：互联网

faiss是Facebook开源的相似性搜索库，为稠密向量提供高效相似度搜索和聚类，支持十亿级别向量的搜索，是目前最为成熟的近似近邻搜索库

faiss不直接提供余弦距离计算，而是提供了欧式距离和点积，利用余弦距离公式，经过L2正则后的向量点积结果即为余弦距离，所以利用faiss计算余弦距离需要先对输入进行L2正则化

安装

参照官方开源安装https://github.com/facebookresearch/faiss/blob/main/INSTALL.md

# CPU-only version
$ conda install -c pytorch faiss-cpu
$ pip install faiss-cpu

# GPU(+CPU) version
$ conda install -c pytorch faiss-gpu
$ pip install faiss-cpu

常规计算余弦距离方式

常规一般使用sklearn包的cosine_similarity计算余弦距离，因为该包自动对向量进行L2正则，所以不要求输入必须为正则结果，代码如下：

## 计算余弦距离
from sklearn.metrics.pairwise import cosine_similarity
from sklearn import preprocessing
def get_cos_result(embeding_library, persons, embeding_search):
    simi = cosine_similarity(embeding_search, embeding_library)
    max_argmin = np.argmax(simi,axis=1)
    search_speaker = [[persons[id],simi[i][id]] for i, id in enumerate(max_argmin)]
    return search_speaker
## 对输入进行正则化，可以不用正则
def l2_normal(embeding):
    return preprocessing.normalize(embeding)

faiss的精确搜索

faiss并不提供计算与余弦距离，只提供了点积计算和欧式距离，所以在计算余弦距离时，需要对输入进行L2正则，代码如下：

import faiss
from faiss import normalize_L2
def faiss_precise_search(embeding_library, persons, embeding_search,topk=1):
    ## 这里也可以使用上文的sklearn的包进行正则
    normalize_L2(embeding_search)
    normalize_L2(embeding_library)
    # faiss.IndexFlatIP是内积 ；faiss.indexFlatL2是欧式距离
    quantizer = faiss.IndexFlatIP(embeding_library.shape[1])
    index = quantizer
    ## 要保证输入为np.float32格式
    index.add(embeding_library.astype(np.float32))
    library = {'persons': persons, 'index': index}
    st = time.time()
    distance,idx = library['index'].search(embeding_search,topk)
    print('precise search:',time.time()-st)
    combined_results = []
    for p in range(len(distance)):
        results = [[library["persons"][i], s] for i, s in zip(idx[p], distance[p]) if s >= 0][0]
        combined_results.append(results)
    return combined_results

faiss快速搜索

faiss提供了多种快速搜索的方式，这里介绍常用的一种加速搜索的方式：倒排索引，这种方式与ES快速搜索的方式类似，需要先使用k-means建立聚类中心，通过查询最近的聚类中心，然后比较聚类中所有向量得到相似向量，这里需要两个超参数，一个是聚类中心num_cells，一个是查找聚类中心的个数num_cells_in_search，具体代码如下

def faiss_fast_search(embeding_library, persons, embeding_search,topk=1):
    normalize_L2(embeding_search)
    normalize_L2(embeding_library)
    d = embeding_library.shape[1]
    num_cells = 50
    num_cells_in_search = 5
    # 声明量化器
    quantizer = faiss.IndexFlatIP(embeding_library.shape[1])
     # faiss.METRIC_INNER_PRODUCT计算内积 faiss.METRIC_L2j计算欧式距离
    index = faiss.IndexIVFFlat(quantizer, d,min(num_cells, len(persons)),faiss.METRIC_INNER_PRODUCT)
    assert not index.is_trained
    index.train(embeding_library.astype(np.float32))
    index.add(embeding_library.astype(np.float32))
    index.nprobe = min(num_cells_in_search,len(persons))
    library = {'persons': persons, 'index': index}
    st = time.time()
    distance, idx = library['index'].search(embeding_search, topk)
    print('fast search:',time.time()-st)
    combined_results = []
    for p in range(len(distance)):
        results = [[library["persons"][i], s] for i, s in zip(idx[p], distance[p]) if s >= 0][0]
        combined_results.append(results)
    return combined_results

整体代码

# -*- coding: utf-8 -*-
import faiss
from faiss import normalize_L2
from sklearn.metrics.pairwise import cosine_similarity
from sklearn import preprocessing
import numpy as np
import time

def l2_normal(embeding):
    return preprocessing.normalize(embeding)

def get_cos_result(embeding_search, persons, embeding_library):
    simi = cosine_similarity(embeding_search, embeding_library)
    max_argmin = np.argmax(simi,axis=1)
    search_speaker = [[persons[id],simi[i][id]] for i, id in enumerate(max_argmin)]
    return search_speaker

def faiss_precise_search(embeding_library, persons, embeding_search):
    normalize_L2(embeding_search)
    normalize_L2(embeding_library)
    # faiss.IndexFlatIP是内积 ；faiss.indexFlatL2是欧式距离
    quantizer = faiss.IndexFlatIP(embeding_library.shape[1])
    index = quantizer
    index.add(embeding_library.astype(np.float32))
    library = {'persons': persons, 'index': index}
    st = time.time()
    distance,idx = library['index'].search(embeding_search,1)
    print('precise search:',time.time()-st)
    combined_results = []
    for p in range(len(distance)):
        results = [[library["persons"][i], s] for i, s in zip(idx[p], distance[p]) if s >= 0][0]
        combined_results.append(results)
    return combined_results

def faiss_fast_search(embeding_library, persons, embeding_search,topk=1):
    normalize_L2(embeding_search)
    normalize_L2(embeding_library)
    num_cells = 500
    num_cells_in_search = 10
    quantizer = faiss.IndexFlatIP(embeding_library.shape[1])
    index = faiss.IndexIVFFlat(quantizer, embeding_library.shape[1],min(num_cells, len(persons)),faiss.METRIC_INNER_PRODUCT) #faiss.METRIC_INNER_PRODUCT计算内积 faiss.METRIC_L2j计算欧式距离
    assert not index.is_trained
    index.train(embeding_library.astype(np.float32))
    index.add(embeding_library.astype(np.float32))
    index.nprobe = min(num_cells_in_search,len(persons))
    library = {'persons': persons, 'index': index}
    st = time.time()
    distance, idx = library['index'].search(embeding_search, topk)
    print('fast search:',time.time()-st)
    combined_results = []
    for p in range(len(distance)):
        results = [[library["persons"][i], s] for i, s in zip(idx[p], distance[p]) if s >= 0][0]
        combined_results.append(results)
    return combined_results

if __name__ == '__main__':
    d = 512
    n_library = 100000
    n_search = 1
    embeding_library = np.random.random((n_library, d)).astype(np.float32)
    persons = ['Speak' + "%0d" % (i + 1) for i in range(n_library)]
    embeding_search = np.random.random((n_search, d)).astype(np.float32)
    print(faiss_fast_search(embeding_library, persons, embeding_search))
    print(faiss_precise_search(embeding_library, persons, embeding_search))
    st = time.time()
    print(get_cos_result(embeding_search, persons, embeding_library))
    en1 = time.time()
    print(en1-st)

标签：index,search,library,余弦,persons,距离,embeding,faiss
来源： https://www.cnblogs.com/peng-yuan/p/15476945.html