{"id":200437,"date":"2023-12-21T10:20:11","date_gmt":"2023-12-21T15:20:11","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=200437"},"modified":"2023-12-21T10:20:59","modified_gmt":"2023-12-21T15:20:59","slug":"vectorize-fuzzy-matching","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/vectorize-fuzzy-matching\/","title":{"rendered":"Vectorize Fuzzy Matching"},"content":{"rendered":"\n

One of the best things about R is its ability to vectorize code. This allows you to run code much faster than you would if you were using a for<\/em> or while<\/em> loop. In this post, we\u2019re going to show you how to use vectorization<\/a> to speed up fuzzy matching. First, a little bit of background will be covered. If you\u2019re familiar with vectorization and \/ or fuzzy matching, feel free to skip further down the post.<\/p>\n\n\n\n

What is vectorization?<\/strong><\/h2>\n\n\n\n

Vectorization works by performing operations on entire vectors, or by extension, matrices, rather than iterating through each element in a collection of objects one at a time. A basic example is adding two vectors together. This can be done like this:<\/p>\n\n\n\n

a <- c(3, 4, 5)\nb <- c(6, 7, 8)\n \nsums <- a + b<\/pre>\n\n\n\n
In this example, sums<\/em> now equals c(9, 11, 13). This is because the addition operator, \u201c+\u201d, was applied pairwise to each vector i.e. the first element of a was added to the first element of b, the second element of a was added to the second element of b, and so on. Vectorization is faster than traditional for loops because it uses parallel operations under the hood. For loops in R, on the other hand, are notoriously slow.<\/p>\n\n\n\n
This example may seem simple, but vectorization can be used much more powerfully to speed up a process like fuzzy matching, the topic of this article.<\/p>\n\n\n\n
What is fuzzy matching?<\/strong><\/h2>\n\n\n\n
Fuzzy matching<\/a> is the process of finding strings that follow similar patterns. For example, suppose you\u2019re trying to join two data sets together on a city field. Data for these cities may be entered into a system manually, allowing for spelling or formatting differences i.e. \u201cMt. Hood\u201d might be coded as \u201cMount Hood\u201d etc. For a human, we can see those are clearly the same, but we need an algorithmic way to determine that so we don\u2019t have to manually go through large numbers of possibilities.<\/p>\n\n\n\n
The stringdist package<\/strong><\/h2>\n\n\n\n
To perform fuzzy matching, we\u2019re going to use a package called stringdist<\/a>. This contains a function we need called stringsim<\/em> which gives a measure of similarity between a pair of strings. This function allows several different algorithms to compare the similarity between two strings, and returns a value between 0 (very dissimilar) and 1 (very similar or equal). For instance:<\/p>\n\n\n\n
# load stringdist package\nrequire(stringdist)\n \n# compare two sample strings\nstringsim(\"this is a test\", \"this is the test\", method = \"jw\")<\/pre>\n\n\n\n
Simplistic Approach<\/strong><\/h2>\n\n\n\n
Alright, so to illustrate how much time vectorization saves, let\u2019s do fuzzy matching in a less clever way. We\u2019re going to use the zipcode<\/em> package to get a sample list of cities across the US. These get stored in the vector, cities<\/em>. Note, since this data set is actually at a zip code level, we\u2019re going to have some duplicates in our vector, but that\u2019s not a concern for this example because we\u2019re trying to demonstrate performance differences between vectorization versus non-vectorization. Thus, our cities<\/em> vector contains 44,336 elements.<\/p>\n\n\n\n
Also, we create a vector below, called input<\/em>, containing the misspelled or adjusted spellings of several cities in Florida (\u201cMt. Dora\u201d vs. \u201cMount Dora\u201d, \u201cSun City, FL\u201d vs. \u201cSun City\u201d etc.).<\/p>\n\n\n\n
# load packages\nrequire(stringdist)\nrequire(zipcode)\ndata(\"zipcode\")\n \ncities <- zipcode$city\n \n# misspell \/ change spellings from a few Florida cities\ninput <- c(\"Centry\", \"Boca R.\", \"Mt. Dora\", \"winterhaven\", \"Sun City, FL\")<\/pre>\n\n\n\n
Now, one way of doing fuzzy matching here would be to loop through each city in our input<\/em> vector, and then loop through each city in the cities<\/em> vector and check the string similarity between each possible pair of strings i.e. check the similarity between the first element in input<\/em> against every single element in cities<\/em>. Since the length of input<\/em> is 5 and the length of cities<\/em> is 44,336, this requires 5 * 44,336 = 221,680 calls to stringsim<\/em>.<\/p>\n\n\n\n
# Naive approach\nstart <- proc.time()\nresults <- c()\nfor(city in input)\n{\n   \n    best_dist <- -Inf\n    for(check in cities)\n    {\n        dist <- stringsim(city, check, method = \"jw\")\n        if(dist > best_dist)\n        {\n            closest_match <- check\n            best_dist <- dist\n           \n        }\n       \n    }\n     \n    results <- append(results, closest_match)\n   \n}\nend <- proc.time()<\/pre>\n\n\n\n
If we run end \u2013 start, we find that this process takes just under 26 seconds. However, let\u2019s see what happens when we ditch the for loops, and use vectorization instead.<\/p>\n\n\n\n
Vectorizing Fuzzy Matching<\/strong><\/h2>\n\n\n\n
# define function to search for best string match in cities\nget_best_match <- function(city, cities = cities)\n{\n     \n    max_index <- which.max(stringsim(city, cities, method = \"jw\"))\n     \n    return(cities[max_index])\n   \n}\n \n \nvector_start <- proc.time()\nvector_results <- sapply(input, function(city) get_best_match(city, cities))\nvector_end <- proc.time()<\/pre>\n\n\n\n
If we run vector_end \u2013 vector_start, we see this only takes 0.03 seconds! The main reason for the speed up can be seen in this line:<\/p>\n\n\n\n
max_index <- which.max(stringsim(city, cities, method = \"jw\"))<\/pre>\n\n\n\n
The inner function, stringsim<\/em>, gets the string similarity between the input city (e.g. \u201cMt. Dora\u201d) versus every element in the cities vector. This is done in one line, rather than using a for or while loop construct. The output of this function call is a vector that shows the similarity measures (between 0 and 1) of the input city versus everything in cities<\/em>.<\/p>\n\n\n\n
We then apply the which.max<\/em> function to get the index of the element in cities that is the closest match to the input city. Using this index, we can get the actual name of the city with the closest match, like below.<\/p>\n\n\n\n
return(cities[max_index])<\/pre>\n\n\n\n
The other reason for the performance increase is using sapply<\/em> to actually loop over the elements in our input vector, passing element in turn to the get_best_match<\/em> function. Since sapply<\/em> is written in C under the hood, it usually runs much faster than a traditional for loop in R.<\/p>\n\n\n\n
That\u2019s it for this post! Check out other posts of mine here: https:\/\/theautomatic.net\/blog\/<\/a>.<\/p>\n\n\n\n
Originally posted on TheAutomatic.net<\/a> blog.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"
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software automation, and ETL. He completed a master\u2019s degree in computer science \/ machine learning, and an undergraduate degree in pure mathematics. Connect with him on LinkedIn: https:\/\/www.linkedin.com\/in\/andrew-treadway-a3b19b103\/In addition to TheAutomatic.net blog, he also teaches in-person courses on Python and R through my NYC meetup: more details.","sameAs":["https:\/\/theautomatic.net\/about-me\/"],"url":"https:\/\/www.interactivebrokers.com\/campus\/author\/andrewtreadway\/"}]}},"jetpack_featured_media_url":"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2020\/11\/binary-background-abstract.jpg","_links":{"self":[{"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/posts\/200437","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/users\/388"}],"replies":[{"embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/comments?post=200437"}],"version-history":[{"count":0,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/posts\/200437\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/media\/67374"}],"wp:attachment":[{"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/media?parent=200437"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/categories?post=200437"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/tags?post=200437"},{"taxonomy":"contributors-categories","embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/contributors-categories?post=200437"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}