姓名匹配运行sparse_dot_topn函数会给我警告：内核重新启动了吗？

问题描述

我正在尝试通过awesome_cossim_top使用余弦相似度将公司名称与政府的公司名称数据库匹配。因此，我将ngrams tf-idf转换为CSR矩阵，然后通过该函数运行它。它不会运行，并且会在每个IDE（Colab，Spyder，PyCharm和Jupyter）上重新启动内核。它根本不起作用。我想知道为什么吗？

import re
from ftfy import fix_text
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neighbors import NearestNeighbors
import difflib
import numpy as np
from sparse_dot_topn import awesome_cossim_topn
from scipy.sparse import csr_matrix
import sparse_dot_topn.sparse_dot_topn as ct

def ngrams(string,n=3):
    string = fix_text(string) # fix text encoding issues
    string = string.encode("ascii",errors="ignore").decode() #remove non ascii chars
    string = string.lower() #make lower case
    chars_to_remove = [")","(",".","|","[","]","{","}","'"]
    rx = '[' + re.escape(''.join(chars_to_remove)) + ']'
    string = re.sub(rx,'',string) #remove the list of chars defined above
    string = string.replace('&','and')
    string = string.replace(',',' ')
    string = string.replace('-',' ')
    string = string.title() # normalise case - capital at start of each word
    string = re.sub(' +',' ',string).strip() # get rid of multiple spaces and replace with a single space
    string = ' '+ string +' ' # pad names for ngrams...
    string = re.sub(r'[,-./]|\sBD',r'',string)
    ngrams = zip(*[string[i:] for i in range(n)])
    
    return [''.join(ngram) for ngram in ngrams]

def awesome_cossim_top(A,B,ntop,lower_bound=0):
    # force A and B as a CSR matrix.
    # If they have already been CSR,there is no overhead
    A = A.tocsr()
    B = B.tocsr()
    M,_ = A.shape
    _,N = B.shape

    idx_dtype = np.int32

    nnz_max = M * ntop

    indptr = np.zeros(M + 1,dtype=idx_dtype)
    indices = np.zeros(nnz_max,dtype=idx_dtype)
    data = np.zeros(nnz_max,dtype=A.dtype)

    ct.sparse_dot_topn(
        M,N,np.asarray(A.indptr,dtype=idx_dtype),np.asarray(A.indices,A.data,np.asarray(B.indptr,np.asarray(B.indices,B.data,lower_bound,indptr,indices,data)

    return csr_matrix((data,indptr),shape=(M,N))

def get_matches_df(sparse_matrix,A,top=100):
    non_zeros = sparse_matrix.nonzero()

    sparserows = non_zeros[0]
    sparsecols = non_zeros[1]

    if top:
        nr_matches = top
    else:
        nr_matches = sparsecols.size

    left_side = np.empty([nr_matches],dtype=object)
    right_side = np.empty([nr_matches],dtype=object)
    similairity = np.zeros(nr_matches)

    for index in range(0,nr_matches):
        left_side[index] = A[sparserows[index]]
        right_side[index] = B[sparsecols[index]]
        similairity[index] = sparse_matrix.data[index]

    return pd.DataFrame({'left_side': left_side,'right_side': right_side,'similairity': similairity})

govdata = pd.read_csv('companydata2018.csv',encoding='utf-8')
hypxdata = pd.read_csv('enerygycomp.csv',encoding='cp1252')

#X = gov Y = hypx
vectoriser = TfidfVectorizer(analyzer=ngrams)

tfidfgov = vectoriser.fit_transform(govdata['CompanyName'])
tfidfhypx = vectoriser.fit_transform(hypxdata['Name'])

matches = awesome_cossim_top(tfidfgov,tfidfhypx.transpose(),1,0)```

解决方法

我猜你的内存不足。您是否尝试过使用较小的数据集？

此外，我认为您应该分别执行拟合和转换步骤：将向量化器与两个系列拟合（例如将它们连接起来），然后通过变换获取两个数据集的 tfidf 矩阵。