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如何使用agrep获得模糊字符串匹配的精确公共“最大距离”值?

agrep string-matching r

7

Eric · 技术社区 · 6 年前

我试图找出使用agrep在两个字符串名称之间进行模糊字符串匹配的最佳精度。

但是,我需要选择一个精度“max.distance”,以便在我尝试匹配的所有字符串中应用相同的值,因为字符串的数量很大。对于我要匹配的每个字符串,都不可能选择最佳精度值“max.distance”。

例如,假设我使用精度“max.distance”作为“0.2”、“0.1”和“0.05”,分别表示“美国银行”和“第一资本银行”。

首先,“美国银行”的“最大距离”为“0.2”、“0.1”和“0.05”:

    > agrep("BANK OF AMERICA CORP",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.2)
     [1] "BANK OF AMERICA/PRIVATE BANK WEST"   "BANK OF AMERICA SECURITIES"         
     [3] "BANK OF AMERICA SEC LLC"             "BANK OF AMERICA SECURITIES LLC"     
     [5] "BANK OF AMERICA NT & SA"             "BANK OF AMERICA CORP"               
     [7] "ALLIANZ OF AMERICA CORP"             "Bank of America Securities/Vice Pre"
     [9] "Bank of America Securities/Investme" "Bank of America/President"          
    [11] "Bank of America Securities LLC/Prin" "Bank of America Securities LLC/Mana"
    [13] "Bank of America Securities LLC/Inve" "Bank of America Securities/Principa"
    [15] "Bank of America Securities LLC/Bank" "Bank of America Sec/Investment Bank"
    [17] "Bank Of America Securities/Managing" "Bank of America/Chairman--Midwest A"
    [19] "Bank of America Securities LLC/Vice" "Bank of America Corporation/Sales C"
    [21] "Bank of America Securities/Broker"   "Bank of America Corporation/Banker" 
    [23] "Bank of America Corporation/Senior"  "Bank of America Securities/Equity R"
    [25] "Bank of America Corporation/Vice Ch" "BANK OF AMERICA CORPORATION"        
    [27] "BANK OF AMERICA HEADQUARTERS"        "BANK OF AMERICA ADMINISTRATION"     
    [29] "BANK OF AMERICA N A"                 "Bank of America/Commercial Banking" 
    [31] "Bank of America Sec./Investment Ban"
    > 
    > agrep("BANK OF AMERICA CORP",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.1)
    [1] "BANK OF AMERICA CORP"                "ALLIANZ OF AMERICA CORP"            
    [3] "Bank of America Corporation/Sales C" "Bank of America Corporation/Banker" 
    [5] "Bank of America Corporation/Senior"  "Bank of America Corporation/Vice Ch"
    [7] "BANK OF AMERICA CORPORATION"        
    > 
    > agrep("BANK OF AMERICA CORP",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.05)
    [1] "BANK OF AMERICA CORP"                "Bank of America Corporation/Sales C"
    [3] "Bank of America Corporation/Banker"  "Bank of America Corporation/Senior" 
    [5] "Bank of America Corporation/Vice Ch" "BANK OF AMERICA CORPORATION"

下面是“最大距离”为“0.2”、“0.1”和“0.05”的“第一资本银行”:

    > agrep("1st Capital Bank",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.2)
      [1] "HURST CAPITAL PARTNERS"             
      [2] "SOY CAPITAL BANK"                   
      [3] "FIRST CAPITOL BANK OF VICTOR"       
      [4] "OSTERWEIS CAPITAL MANAGEMENT"       
      [5] "1ST NATIONAL BANK"                  
      [6] "FIRST CAPITAL BANK"                 
      [7] "SEATTLE 1ST NAT'L BANK"             
      [8] "FIELD POINT CAPITAL MANAGEMENT"     
      [9] "SUMMERSET CAPITAL MANAGEMENT"       
     [10] "AMERIQUEST CAPITAL ASSOC"           
     [11] "BB&T CAPITAL MARKETS"               
     [12] "HUGHES CAPITAL MANAGEMENT"          
     [13] "WELLS CAPITAL MANAGEMENT"           
     [14] "SUPERIOR ST CAPITAL ADVISORS"       
     [15] "ORMES CAPITAL MARKETS INC"          
     [16] "1ST NAT'L BANK OF IL"               
     [17] "ADVENT CAPITAL MANAGEMENT"          
     [18] "1ST CAPITOL BANK"                   
     [19] "BIONDI REISS CAPITAL MANAGEMENT"    
     [20] "CCYBYS CAPITAL MARKETS"             
     [21] "SEACOAST CAPITAL PARTNERS"          
     [22] "DOUGLAS CAPITAL MANAGEMENT"         
     [23] "HIGHFIELDS CAPITAL MANAGEMENT"      
     [24] "PRECEPT CAPITAL MANAGEMENT LP"      
     [25] "AUGUST CAPITAL MANAGEMENT"          
     [26] "SAKSA CAPITAL MANAGEMENT"           
     [27] "IMS CAPITAL MANAGEMENT"             
     [28] "TRENT CAPITAL MANAGEMENT"           
     [29] "Ormes Capital Management"           
     [30] "GARNET CAPITAL MANAGEMENT LLC"      
     [31] "INTERFASE CAPITAL MANAGERS"         
     [32] "RJS CAPITAL MANAGEMENT INC"         
     [33] "1ST NATIONAL BANK OF DE KALB"       
     [34] "1ST NAT'L BANK OF PHILLIPS CO"      
     [35] "1ST NAT'L BANK OF OKLAHOMA"         
     [36] "PROGRESS CAPITAL MANAGEMENT INC"    
     [37] "CAPITAL BANK & TRUST"               
     [38] "1ST NATL BANK"                      
     [39] "ASB Capital Management/Real Estate" 
     [40] "Sears Capital Management"           
     [41] "Osterweis Capital Management/Invest"
     [42] "Cerberus Capital Management LP/Asse"
     [43] "LVS Capital Management/President"   
     [44] "1st Central Bank/Banker"            
     [45] "Summit Capital Management"          
     [46] "Orwes Capital Markets/Stockbroker"  
     [47] "Ormes Capital Management/Investment"
     [48] "Nevis Capital Management/Investment"
     [49] "Duncan Hurst Capital Management"    
     [50] "Progress Capital Management/Preside"
     [51] "Cerberus Capital Management LP"     
     [52] "Wit Capital/Banker"                 
     [53] "Ormes Capital Markets Inc."         
     [54] "Ormes Capital Markets/President & C"
     [55] "Berents & Hess Capital Management"  
     [56] "Progress Capital Management/Venture"
     [57] "First Capital Bank of KY"           
     [58] "Foothill Capital/Banker"            
     [59] "Pequot Capital Management/Equity Re"
     [60] "First Dominion Capital/Banking"     
     [61] "Greenwhich Capital/Banker"          
     [62] "Veritas Capital Management/Banker"  
     [63] "Veritas Capital Management/Investme"
     [64] "Lesese Capital Management/Investmen"
     [65] "Douglas Capital Management/Investme"
     [66] "FIRST NATINAL BANK OF AMARILLO"     
     [67] "NEVIS CAPITAL MANAGEMENT"           
     [68] "VERITAS CAPITAL MANAGEMENT"         
     [69] "SIEBERT CAPITAL MARKETS"            
     [70] "HOURGLASS CAPITAL MANAGEMENT"       
     [71] "1ST NATIONAL BANK DALHART"          
     [72] "TEXAS CAPITAL BANK"                 
     [73] "NICHOLAS CAPITAL MANAGEMENT"        
     [74] "CERBUS CAPITAL MANAGEMENT"          
     [75] "CROESUS CAPITAL MANAGEMENT"         
     [76] "EAST WEST CAPITAL ASSOCIATES INC"   
     [77] "PRENDERGAST CAPITAL MANAGEMENT"     
     [78] "NANTUCKET CAPITAL MANAGEMENT"       
     [79] "1ST NATIONAL BANK TEMPLE"           
     [80] "ENTRUST CAPITAL INC"                
     [81] "1ST NATIONAL BANK OF IL"            
     [82] "SIMMS CAPITAL MANAGEMENT"           
     [83] "FIRST CAPITAL ADVISORS"             
     [84] "FIRST CAPITAL MANAGEMENT LTD"       
     [85] "1ST NATIONAL BANK & TRUST"          
     [86] "PENTECOST CAPITAL MANAGEMENT INC"   
     [87] "EAST-WEST CAPITAL ASSOCIATES"       
     [88] "1ST NAT'L BANK OF JOLIET"           
     [89] "FIRST CAPITOL BANK OF VICTO"        
     [90] "FIRST CAPITAL FINANCIAL"            
     [91] "PACIFIC COAST CAPITAL PARTNERS"     
     [92] "FIRST CAPITOL BANK"                 
     [93] "FIRST CAPITAL ENGINEERING"          
     [94] "MIDWEST CAPITOL MANAGEMENT"         
     [95] "PEQUOT CAPITAL MANAGEMENT"          
     [96] "AGGOTT CAPITAL MANAGEMENT"          
     [97] "SIMMS CAPITAL MANAGEMENT INC"       
     [98] "PHILLIPS CAPITAL MANAGEMENT LLC"    
     [99] "1ST NATIONAL BANK OF COLD SP"       
    [100] "SOY CAPITOL BANK"                   
    > 
    > agrep("1st Capital Bank",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.1)
    [1] "FIRST CAPITOL BANK OF VICTOR" "FIRST CAPITAL BANK"          
    [3] "1ST CAPITOL BANK"             "First Capital Bank of KY"    
    [5] "TEXAS CAPITAL BANK"           "FIRST CAPITOL BANK OF VICTO" 
    [7] "FIRST CAPITOL BANK"          
    > 
    > agrep("1st Capital Bank",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.05)
    [1] "FIRST CAPITAL BANK"       "1ST CAPITOL BANK"        
    [3] "First Capital Bank of KY"

如您所见,很难找到适用于每个字符串的“max.distance”的通用精度值,例如“美国银行”和“第一资本银行”。另外,除了这两个名字之外,我还有更多的公司名称,这就是为什么我很难找到模糊字符串匹配的通用精度值和命令的原因。

C1999 U 0的原始数据文件太大,无法附加,因此我认为仅使用上面所示的输出值就足以进行复制。

我知道有几个子类别需要操作,比如成本、替换、插入等等,但是它们只改变“最大距离”值本身并没有什么区别。

如果我能得到帮助,我会非常感激的!

2 回复 | 直到 6 年前

1

2

Jon Spring 6 年前

如前所述,这似乎是一个无法解决的问题,没有一个最大距离对所有输入字符串都有效。

可能值得尝试这样的方法 tf-idf 识别出你的弦的不寻常,并将你的最大距离调整到这个程度。因此,“Ziggurat Mutual”可能比“First Bank National”有更大的变化余地,后者更通用。

您还可以考虑使用fuzzyjoin包,该包提供了一些尝试不同选项的快速方法。例如,您可以尝试:

df <- c("HURST CAPITAL PARTNERS", "SOY CAPITAL BANK", "FIRST CAPITOL BANK OF VICTOR", "OSTERWEIS CAPITAL MANAGEMENT", "1ST NATIONAL BANK", "FIRST CAPITAL BANK", "SEATTLE 1ST NAT'L BANK", "FIELD POINT CAPITAL MANAGEMENT", "SUMMERSET CAPITAL MANAGEMENT", "AMERIQUEST CAPITAL ASSOC", "BB&T CAPITAL MARKETS", "HUGHES CAPITAL MANAGEMENT", "WELLS CAPITAL MANAGEMENT", "SUPERIOR ST CAPITAL ADVISORS", "ORMES CAPITAL MARKETS INC", "1ST NAT'L BANK OF IL", "ADVENT CAPITAL MANAGEMENT", "1ST CAPITOL BANK", "BIONDI REISS CAPITAL MANAGEMENT", "CCYBYS CAPITAL MARKETS", "SEACOAST CAPITAL PARTNERS", "DOUGLAS CAPITAL MANAGEMENT", "HIGHFIELDS CAPITAL MANAGEMENT", "PRECEPT CAPITAL MANAGEMENT LP", "AUGUST CAPITAL MANAGEMENT", "SAKSA CAPITAL MANAGEMENT", "IMS CAPITAL MANAGEMENT", "TRENT CAPITAL MANAGEMENT", "Ormes Capital Management", "GARNET CAPITAL MANAGEMENT LLC", "INTERFASE CAPITAL MANAGERS", "RJS CAPITAL MANAGEMENT INC", "1ST NATIONAL BANK OF DE KALB", "1ST NAT'L BANK OF PHILLIPS CO", "1ST NAT'L BANK OF OKLAHOMA", "PROGRESS CAPITAL MANAGEMENT INC", "CAPITAL BANK & TRUST", "1ST NATL BANK", "ASB Capital Management/Real Estate", "Sears Capital Management", "Osterweis Capital Management/Invest", "Cerberus Capital Management LP/Asse", "LVS Capital Management/President", "1st Central Bank/Banker", "Summit Capital Management", "Orwes Capital Markets/Stockbroker", "Ormes Capital Management/Investment", "Nevis Capital Management/Investment", "Duncan Hurst Capital Management", "Progress Capital Management/Preside", "Cerberus Capital Management LP", "Wit Capital/Banker", "Ormes Capital Markets Inc.", "Ormes Capital Markets/President & C", "Berents & Hess Capital Management", "Progress Capital Management/Venture", "First Capital Bank of KY", "Foothill Capital/Banker", "Pequot Capital Management/Equity Re", "First Dominion Capital/Banking", "Greenwhich Capital/Banker", "Veritas Capital Management/Banker", "Veritas Capital Management/Investme", "Lesese Capital Management/Investmen", "Douglas Capital Management/Investme", "FIRST NATINAL BANK OF AMARILLO", "NEVIS CAPITAL MANAGEMENT", "VERITAS CAPITAL MANAGEMENT", "SIEBERT CAPITAL MARKETS", "HOURGLASS CAPITAL MANAGEMENT", "1ST NATIONAL BANK DALHART", "TEXAS CAPITAL BANK", "NICHOLAS CAPITAL MANAGEMENT", "CERBUS CAPITAL MANAGEMENT", "CROESUS CAPITAL MANAGEMENT", "EAST WEST CAPITAL ASSOCIATES INC", "PRENDERGAST CAPITAL MANAGEMENT", "NANTUCKET CAPITAL MANAGEMENT", "1ST NATIONAL BANK TEMPLE", "ENTRUST CAPITAL INC", "1ST NATIONAL BANK OF IL", "SIMMS CAPITAL MANAGEMENT", "FIRST CAPITAL ADVISORS", "FIRST CAPITAL MANAGEMENT LTD", "1ST NATIONAL BANK & TRUST", "PENTECOST CAPITAL MANAGEMENT INC", "EAST-WEST CAPITAL ASSOCIATES", "1ST NAT'L BANK OF JOLIET", "FIRST CAPITOL BANK OF VICTO", "FIRST CAPITAL FINANCIAL", "PACIFIC COAST CAPITAL PARTNERS", "FIRST CAPITOL BANK", "FIRST CAPITAL ENGINEERING", "MIDWEST CAPITOL MANAGEMENT", "PEQUOT CAPITAL MANAGEMENT", "AGGOTT CAPITAL MANAGEMENT", "SIMMS CAPITAL MANAGEMENT INC", "PHILLIPS CAPITAL MANAGEMENT LLC", "1ST NATIONAL BANK OF COLD SP", "SOY CAPITOL BANK")

library(dplyr); library(fuzzyjoin)
df <- df %>% as_data_frame()

df %>%
  # Allowable methods include osa, lv, dl, hamming, lcs, qgram, 
  #    cosine, jaccard, jw, soundex
  fuzzyjoin::stringdist_inner_join(df, method = "lv", distance_col = "distance", max_dist = 4) %>%
  filter(distance > 0)

Joining by: "value"
# A tibble: 70 x 3
   value.x                      value.y                     distance
   <chr>                        <chr>                          <dbl>
 1 SOY CAPITAL BANK             1ST CAPITOL BANK                   4
 2 SOY CAPITAL BANK             SOY CAPITOL BANK                   1
 3 FIRST CAPITOL BANK OF VICTOR FIRST CAPITOL BANK OF VICTO        1
 4 1ST NATIONAL BANK            1ST NATL BANK                      4
 5 FIRST CAPITAL BANK           1ST CAPITOL BANK                   4
 6 FIRST CAPITAL BANK           FIRST CAPITOL BANK                 1
 7 HUGHES CAPITAL MANAGEMENT    DOUGLAS CAPITAL MANAGEMENT         4
 8 HUGHES CAPITAL MANAGEMENT    AUGUST CAPITAL MANAGEMENT          4
 9 WELLS CAPITAL MANAGEMENT     IMS CAPITAL MANAGEMENT             4
10 WELLS CAPITAL MANAGEMENT     NEVIS CAPITAL MANAGEMENT           3

…在示例列表中尝试潜在的非精确匹配。

2

3

Benjamin Christoffersen 6 年前

一个问题 agrep 是这样吗 grep 如中所述 help("grep")

由于有人不小心阅读了描述,甚至提交了一个bug报告,请注意,这与 x (就像 格雷普 不包括所有元素。也见 adist 包内 乌迪斯 ,可以选择返回匹配子字符串的偏移量。

在后一个示例中,这似乎是个问题,因为您有许多名称包含“capital”或“bank”或两者。我要做的是用它来计算 Levenshtein distance (这是什么? 格雷普 是或是一个广义的版本和 仅适用于子字符串 )取距离最短的。例如。,

C1999 <- c("HURST CAPITAL PARTNERS", "SOY CAPITAL BANK", "FIRST CAPITOL BANK OF VICTOR", "OSTERWEIS CAPITAL MANAGEMENT", "1ST NATIONAL BANK", "FIRST CAPITAL BANK", "SEATTLE 1ST NAT'L BANK", "FIELD POINT CAPITAL MANAGEMENT", "SUMMERSET CAPITAL MANAGEMENT", "AMERIQUEST CAPITAL ASSOC", "BB&T CAPITAL MARKETS", "HUGHES CAPITAL MANAGEMENT", "WELLS CAPITAL MANAGEMENT", "SUPERIOR ST CAPITAL ADVISORS", "ORMES CAPITAL MARKETS INC", "1ST NAT'L BANK OF IL", "ADVENT CAPITAL MANAGEMENT", "1ST CAPITOL BANK", "BIONDI REISS CAPITAL MANAGEMENT", "CCYBYS CAPITAL MARKETS", "SEACOAST CAPITAL PARTNERS", "DOUGLAS CAPITAL MANAGEMENT", "HIGHFIELDS CAPITAL MANAGEMENT", "PRECEPT CAPITAL MANAGEMENT LP", "AUGUST CAPITAL MANAGEMENT", "SAKSA CAPITAL MANAGEMENT", "IMS CAPITAL MANAGEMENT", "TRENT CAPITAL MANAGEMENT", "Ormes Capital Management", "GARNET CAPITAL MANAGEMENT LLC", "INTERFASE CAPITAL MANAGERS", "RJS CAPITAL MANAGEMENT INC", "1ST NATIONAL BANK OF DE KALB", "1ST NAT'L BANK OF PHILLIPS CO", "1ST NAT'L BANK OF OKLAHOMA", "PROGRESS CAPITAL MANAGEMENT INC", "CAPITAL BANK & TRUST", "1ST NATL BANK", "ASB Capital Management/Real Estate", "Sears Capital Management", "Osterweis Capital Management/Invest", "Cerberus Capital Management LP/Asse", "LVS Capital Management/President", "1st Central Bank/Banker", "Summit Capital Management", "Orwes Capital Markets/Stockbroker", "Ormes Capital Management/Investment", "Nevis Capital Management/Investment", "Duncan Hurst Capital Management", "Progress Capital Management/Preside", "Cerberus Capital Management LP", "Wit Capital/Banker", "Ormes Capital Markets Inc.", "Ormes Capital Markets/President & C", "Berents & Hess Capital Management", "Progress Capital Management/Venture", "First Capital Bank of KY", "Foothill Capital/Banker", "Pequot Capital Management/Equity Re", "First Dominion Capital/Banking", "Greenwhich Capital/Banker", "Veritas Capital Management/Banker", "Veritas Capital Management/Investme", "Lesese Capital Management/Investmen", "Douglas Capital Management/Investme", "FIRST NATINAL BANK OF AMARILLO", "NEVIS CAPITAL MANAGEMENT", "VERITAS CAPITAL MANAGEMENT", "SIEBERT CAPITAL MARKETS", "HOURGLASS CAPITAL MANAGEMENT", "1ST NATIONAL BANK DALHART", "TEXAS CAPITAL BANK", "NICHOLAS CAPITAL MANAGEMENT", "CERBUS CAPITAL MANAGEMENT", "CROESUS CAPITAL MANAGEMENT", "EAST WEST CAPITAL ASSOCIATES INC", "PRENDERGAST CAPITAL MANAGEMENT", "NANTUCKET CAPITAL MANAGEMENT", "1ST NATIONAL BANK TEMPLE", "ENTRUST CAPITAL INC", "1ST NATIONAL BANK OF IL", "SIMMS CAPITAL MANAGEMENT", "FIRST CAPITAL ADVISORS", "FIRST CAPITAL MANAGEMENT LTD", "1ST NATIONAL BANK & TRUST", "PENTECOST CAPITAL MANAGEMENT INC", "EAST-WEST CAPITAL ASSOCIATES", "1ST NAT'L BANK OF JOLIET", "FIRST CAPITOL BANK OF VICTO", "FIRST CAPITAL FINANCIAL", "PACIFIC COAST CAPITAL PARTNERS", "FIRST CAPITOL BANK", "FIRST CAPITAL ENGINEERING", "MIDWEST CAPITOL MANAGEMENT", "PEQUOT CAPITAL MANAGEMENT", "AGGOTT CAPITAL MANAGEMENT", "SIMMS CAPITAL MANAGEMENT INC", "PHILLIPS CAPITAL MANAGEMENT LLC", "1ST NATIONAL BANK OF COLD SP", "SOY CAPITOL BANK")

func <- function(x, y, tol = 0L){
  require(stringdist)
  dista <- stringdist::stringdist(x, y, method = "lv")
  min_dista <- min(dista)
  y[dista <= min_dista + tol]
}
func("1st Capital Bank", C1999)
#R [1] "Wit Capital/Banker"
func("1st Capital Bank", C1999, 4L)
#R [1] "Wit Capital/Banker"       "First Capital Bank of KY"
func("1st Capital Bank", C1999, 10L)
#R  [1] "SOY CAPITAL BANK"           "1ST NATIONAL BANK"         
#R  [3] "FIRST CAPITAL BANK"         "1ST CAPITOL BANK"          
#R  [5] "Ormes Capital Management"   "1ST NATL BANK"             
#R  [7] "Sears Capital Management"   "1st Central Bank/Banker"   
#R  [9] "Summit Capital Management"  "Wit Capital/Banker"        
#R [11] "Ormes Capital Markets Inc." "First Capital Bank of KY"  
#R [13] "Foothill Capital/Banker"    "Greenwhich Capital/Banker" 
#R [15] "TEXAS CAPITAL BANK"         "FIRST CAPITOL BANK"        
#R [17] "SOY CAPITOL BANK" 

# ignoring cases
func <- function(x, y, tol = 0L){
  require(stringdist)
  dista <- stringdist::stringdist(tolower(x), tolower(y), method = "lv")
  min_dista <- min(dista)
  y[dista <= min_dista + tol]
}
func("1st Capital Bank", C1999, 0L)
#R [1] "1ST CAPITOL BANK"

这个 tol 中的参数 func 控制是否要包括以下示例: 托尔 离拉文斯坦最小距离更远。我知道我没有准确回答你的要求( 如何获得模糊字符串匹配的精确公共__max.distance__值 格雷普 ? )但我想我的答案可能是你想要的。

我用 stringdist::stringdist 而不是 广告作者 因为前者似乎更快。它仍然可以有点慢,我希望在那里有一个R包,你可以设置一个最大的距离,但我没有遇到这样的包。这可以使计算(然后封顶)列文斯坦距离更快。