高效jaccard相似矩阵
我想要一种有效计算高效jaccard相似矩阵,r,text-mining,tm,slam,R,Text Mining,Tm,Slam,我想要一种有效计算tm::DocumentTermMatrix文档之间的Jaccard相似性的方法。我可以通过slam包为余弦相似性做一些类似的事情,如我在交叉验证中遇到的那样,这是R特定的,但关于矩阵代数不一定是最有效的途径。我尝试使用更高效的slam函数实现该解决方案,但没有得到与使用效率较低的方法(将DTM强制为矩阵并使用proxy::dist时相同的解决方案 如何有效地计算R中大型DocumentTermMatrix文档之间的Jaccard相似性 #数据和包装 library(Matri
tm::DocumentTermMatrix
文档之间的Jaccard相似性的方法。我可以通过slam包为余弦相似性做一些类似的事情,如我在交叉验证中遇到的那样,这是R特定的,但关于矩阵代数不一定是最有效的途径。我尝试使用更高效的slam函数实现该解决方案,但没有得到与使用效率较低的方法(将DTM强制为矩阵并使用proxy::dist
时相同的解决方案
如何有效地计算R中大型DocumentTermMatrix文档之间的Jaccard相似性
#数据和包装
library(Matrix);library(proxy);library(tm);library(slam);library(Matrix)
mat <- structure(list(i = c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, 1L,
2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L), j = c(1L, 1L, 2L, 2L, 3L, 3L,
4L, 4L, 4L, 5L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L), v = c(1,
1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1), nrow = 4L,
ncol = 12L, dimnames = structure(list(Docs = c("1", "2",
"3", "4"), Terms = c("computer", "is", "fun", "not", "too",
"no", "it's", "dumb", "what", "should", "we", "do")), .Names = c("Docs",
"Terms"))), .Names = c("i", "j", "v", "nrow", "ncol", "dimnames"
), class = c("DocumentTermMatrix", "simple_triplet_matrix"), weighting = c("term frequency",
"tf"))
#我的尝试
proxy::dist(as.matrix(mat), method = 'jaccard')
## 1 2 3
## 2 0.000
## 3 0.875 0.875
## 4 1.000 1.000 1.000
A <- slam::tcrossprod_simple_triplet_matrix(mat)
im <- which(A > 0, arr.ind=TRUE)
b <- slam::row_sums(mat)
Aim <- A[im]
stats::as.dist(Matrix::sparseMatrix(
i = im[,1],
j = im[,2],
x = Aim / (b[im[,1]] + b[im[,2]] - Aim),
dims = dim(A)
))
## 1 2 3
## 2 2.0
## 3 0.1 0.1
## 4 0.0 0.0 0.0
我希望元素1和2的距离为0,元素3比元素1和4更接近元素1(我希望距离最远,因为没有共享单词),如proxy::dist
解决方案中所示
编辑
请注意,即使在中型DTM上,矩阵也会变得巨大。这里有一个关于纯素食的例子。注:4分钟求解,其中余弦相似性约为5秒
library(qdap); library(quanteda);library(vegan);library(slam)
x <- quanteda::convert(quanteda::dfm(rep(pres_debates2012$dialogue), stem = FALSE,
verbose = FALSE, removeNumbers = FALSE), to = 'tm')
## <<DocumentTermMatrix (documents: 2912, terms: 3368)>>
## Non-/sparse entries: 37836/9769780
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency (tf)
tic <- Sys.time()
jaccard_dist_mat <- vegan::vegdist(as.matrix(x), method = 'jaccard')
Sys.time() - tic #Time difference of 4.01837 mins
tic <- Sys.time()
tdm <- t(x)
cosine_dist_mat <- 1 - crossprod_simple_triplet_matrix(tdm)/(sqrt(col_sums(tdm^2) %*% t(col_sums(tdm^2))))
Sys.time() - tic #Time difference of 5.024992 secs
库(qdap);图书馆(quanteda);图书馆(素食主义者);图书馆(slam)
从vegan
套餐中选择vegdist()
怎么样?
它使用的速度约为proxy的10倍:
library(vegan)
vegdist(as.matrix(mat), method = 'jaccard')
## 1 2 3
## 2 0.0
## 3 0.9 0.9
## 4 1.0 1.0 1.0
library(microbenchmark)
matt <- as.matrix(mat)
microbenchmark(proxy::dist(matt, method = 'jaccard'),
vegdist(matt, method = 'jaccard'))
## Unit: microseconds
## expr min lq mean
## proxy::dist(matt, method = "jaccard") 4879.338 4995.2755 5133.9305
## vegdist(matt, method = "jaccard") 587.935 633.2625 703.8335
## median uq max neval
## 5069.203 5157.520 7549.346 100
## 671.466 723.569 1305.357 100
library(素食主义者)
vegdist(如.matrix(mat),方法='jaccard')
## 1 2 3
## 2 0.0
## 3 0.9 0.9
## 4 1.0 1.0 1.0
图书馆(微基准)
matt使用stringdist
包中的stringdistmatrix
,并使用nthread
选项并行运行,可以大大提高速度。平均比余弦相似性测试慢6秒
library(qdap)
library(slam)
library(stringdist)
data(pres_debates2012)
x <- quanteda::convert(quanteda::dfm(rep(pres_debates2012$dialogue), stem = FALSE,
verbose = FALSE, removeNumbers = FALSE), to = 'tm')
tic <- Sys.time()
tdm <- t(x)
cosine_dist_mat <- 1 - crossprod_simple_triplet_matrix(tdm)/(sqrt(col_sums(tdm^2) %*% t(col_sums(tdm^2))))
Sys.time() - tic #Time difference of 4.069233 secs
tic <- Sys.time()
t <- stringdistmatrix(pres_debates2012$dialogue, method = "jaccard", nthread = 4)
Sys.time() - tic #Time difference of 10.18158 secs
库(qdap)
图书馆(slam)
图书馆(stringdist)
数据(pres_debates2012)
xJaccard度量值是集合之间的度量值,输入矩阵应为二进制。报告说:
如果是一袋文字DTM
这不是常用值的数量
library(text2vec)
library(magrittr)
library(Matrix)
jaccard_similarity <- function(m) {
A <- tcrossprod(m)
im <- which(A > 0, arr.ind=TRUE, useNames = F)
b <- rowSums(m)
Aim <- A[im]
sparseMatrix(
i = im[,1],
j = im[,2],
x = Aim / (b[im[,1]] + b[im[,2]] - Aim),
dims = dim(A)
)
}
jaccard_distance <- function(m) {
1 - jaccard_similarity(m)
}
cosine <- function(m) {
m_normalized <- m / sqrt(rowSums(m ^ 2))
tcrossprod(m_normalized)
}
这是一个玩具的例子。如果你把它扩大到一个更大的范围,素食主义者也会在速度上受到影响。请看我在作品中的时间安排。我不确定我是否理解你的评论。我的回答到底错了什么?它产生了正确的jaccard相似性,并且运行速度非常快。如果我的评论看起来太粗鲁,我很抱歉。调整答案。非常感谢您的帮助,PS喜欢Text2Vec的新添加内容,请参阅我的编辑。距离即将推出的text2vec 0.4更远。
## common values:
A = tcrossprod(m)
library(text2vec)
library(magrittr)
library(Matrix)
jaccard_similarity <- function(m) {
A <- tcrossprod(m)
im <- which(A > 0, arr.ind=TRUE, useNames = F)
b <- rowSums(m)
Aim <- A[im]
sparseMatrix(
i = im[,1],
j = im[,2],
x = Aim / (b[im[,1]] + b[im[,2]] - Aim),
dims = dim(A)
)
}
jaccard_distance <- function(m) {
1 - jaccard_similarity(m)
}
cosine <- function(m) {
m_normalized <- m / sqrt(rowSums(m ^ 2))
tcrossprod(m_normalized)
}
data("movie_review")
tokens <- movie_review$review %>% tolower %>% word_tokenizer
dtm <- create_dtm(itoken(tokens), hash_vectorizer(hash_size = 2**16))
dim(dtm)
# 5000 65536
system.time(dmt_cos <- cosine(dtm))
# user system elapsed
# 2.524 0.169 2.693
system.time( {
dtm_binary <- transform_binary(dtm)
# or simply
# dtm_binary <- sign(dtm)
dtm_jac <- jaccard_similarity(dtm_binary)
})
# user system elapsed
# 11.398 1.599 12.996
max(dtm_jac)
# 1
dim(dtm_jac)
# 5000 5000
jaccard_dist_text2vec_04 <- function(x, y = NULL, format = 'dgCMatrix') {
if (!inherits(x, 'sparseMatrix'))
stop("at the moment jaccard distance defined only for sparse matrices")
# union x
rs_x = rowSums(x)
if (is.null(y)) {
# intersect x
RESULT = tcrossprod(x)
rs_y = rs_x
} else {
if (!inherits(y, 'sparseMatrix'))
stop("at the moment jaccard distance defined only for sparse matrices")
# intersect x y
RESULT = tcrossprod(x, y)
# union y
rs_y = rowSums(y)
}
RESULT = as(RESULT, 'dgTMatrix')
# add 1 to indices because of zero-based indices in sparse matrices
# 1 - (...) because we calculate distance, not similarity
RESULT@x <- 1 - RESULT@x / (rs_x[RESULT@i + 1L] + rs_y[RESULT@j + 1L] - RESULT@x)
if (!inherits(RESULT, format))
RESULT = as(RESULT, format)
RESULT
}
system.time( {
dtm_binary <- transform_binary(dtm)
dtm_jac <-jaccard_dist(dtm_binary, format = 'dgTMatrix')
})
# user system elapsed
# 4.075 0.517 4.593
system.time( {
dtm_binary <- transform_binary(dtm)
dtm_jac <-jaccard_dist(dtm_binary, format = 'dgCMatrix')
})
# user system elapsed
# 6.571 0.939 7.516