R中余弦相似度矩阵的前N个值

Question

如何获得如下所示的余弦相似度矩阵的顶部对：

southpark_matrix <- structure(c(0, 0.165272735625452, 0.386480286121192, 0.170696960480773, 
0.0869562860988618, 0.165272735625452, 0, 0.251690602341816, 
0.472701602991984, 0.137486001150133, 0.386480286121192, 0.251690602341816, 
0, 0.255849200006255, 0.0972813221214626, 0.170696960480773, 
0.472701602991984, 0.255849200006255, 0, 0.156449701347234, 0.0869562860988618, 
0.137486001150133, 0.0972813221214626, 0.156449701347234, 0), .Dim = c(5L, 
5L), .Dimnames = list(Docs = c("Mr. Garrison_2", "Cartman_3", 
"Mr. Garrison_3", "Cartman_4", "Jimbo_5"), Docs = c("Mr. Garrison_2", 
"Cartman_3", "Mr. Garrison_3", "Cartman_4", "Jimbo_5")))

southpark_matrix

                Docs
Docs             Mr. Garrison_2 Cartman_3 Mr. Garrison_3 Cartman_4    Jimbo_5
  Mr. Garrison_2     0.00000000 0.1652727     0.38648029 0.1706970 0.08695629
  Cartman_3          0.16527274 0.0000000     0.25169060 0.4727016 0.13748600
  Mr. Garrison_3     0.38648029 0.2516906     0.00000000 0.2558492 0.09728132
  Cartman_4          0.17069696 0.4727016     0.25584920 0.0000000 0.15644970
  Jimbo_5            0.08695629 0.1374860     0.09728132 0.1564497 0.00000000

如何获得前 2 对？

在这个例子中，前 2 对是。在我的实际示例中，我有超过 100 列和行。

Cartman_3 Cartman_4             0.4727016
Mr. Garrison_2 Mr. Garrison_3   0.38648029

Answer 1

我这样做的方法是将矩阵转换为 tibble。我们可以按照此处的步骤将矩阵的上三角部分转换为 2 列中的数据框（参见此处：）。

在此之后，我们可以简单地使用由我们的值加权的 top_n(2, val) 函数。此步骤的另一种方法是使用 arrange(desc(val)) 按降序排列值，然后使用 head(2) 函数获取前 2 个值。

我在下面制作了我的方法的代表

library(tidyverse)

southpark_matrix <- structure(c(0, 0.165272735625452, 0.386480286121192, 0.170696960480773, 
                                0.0869562860988618, 0.165272735625452, 0, 0.251690602341816, 
                                0.472701602991984, 0.137486001150133, 0.386480286121192, 0.251690602341816, 
                                0, 0.255849200006255, 0.0972813221214626, 0.170696960480773, 
                                0.472701602991984, 0.255849200006255, 0, 0.156449701347234, 0.0869562860988618, 
                                0.137486001150133, 0.0972813221214626, 0.156449701347234, 0), .Dim = c(5L, 
                                                                                                       5L), .Dimnames = list(Docs = c("Mr. Garrison_2", "Cartman_3", 
                                                                                                                                      "Mr. Garrison_3", "Cartman_4", "Jimbo_5"), Docs = c("Mr. Garrison_2", 
                                                                                                                                                                                          "Cartman_3", "Mr. Garrison_3", "Cartman_4", "Jimbo_5")))

# Convert the matrix to an upper diagonal form
ind <- which(upper.tri(southpark_matrix, diag = TRUE), arr.ind = TRUE)
dimnam <- dimnames(southpark_matrix)
df <- data.frame(row = dimnam[[1]][ind[, 1]],
           col = dimnam[[2]][ind[, 2]],
           val = southpark_matrix[ind])
#top n method
df %>%
  tibble() %>% 
  top_n(2, val)
#> # A tibble: 2 x 3
#>   row            col              val
#>   <chr>          <chr>          <dbl>
#> 1 Mr. Garrison_2 Mr. Garrison_3 0.386
#> 2 Cartman_3      Cartman_4      0.473

#arrange and head method
df %>% 
  arrange(desc(val)) %>% 
  head(2)
#> # A tibble: 2 x 3
#>   row            col              val
#>   <chr>          <chr>          <dbl>
#> 1 Cartman_3      Cartman_4      0.473
#> 2 Mr. Garrison_2 Mr. Garrison_3 0.386

^{由 reprex package (v2.0.0)}

于 2021-04-04 创建

Answer 2

与lapply:

N <- 2
best <- head(sort(southpark_matrix[upper.tri(southpark_matrix)], decreasing = TRUE),N)
lapply(best, function(x) {
  list(similarity = x, names = rownames(which(southpark_matrix == x, arr.ind = TRUE)))
})
#> [[1]]
#> [[1]]$similarity
#> [1] 0.4727016
#> 
#> [[1]]$names
#> [1] "Cartman_4" "Cartman_3"
#> 
#> 
#> [[2]]
#> [[2]]$similarity
#> [1] 0.3864803
#> 
#> [[2]]$names
#> [1] "Mr. Garrison_3" "Mr. Garrison_2"

R中余弦相似度矩阵的前N个值

Top N Values of Cosine Similarity Matrix in R

r

matrix

cosine-similarity