knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(levitate)
This article walks through an example of using levitate
to compare text strings in the wild, and
aims to give you a feel for the pros and cons of the different string similarity measures provided
by the package.
levitate
comes with hotel_rooms
dataset that contains descriptions of the same hotel rooms from
two different websites, Expedia and Booking.com. The list was compiled by
Susan Li - all credit to her for the work.
head(hotel_rooms)
Let's add columns to the dataset showing how the different algorithms score the two strings.
df <- hotel_rooms df$lev_ratio <- lev_ratio(df$expedia, df$booking) df$lev_partial_ratio <- lev_partial_ratio(df$expedia, df$booking) df$lev_token_sort_ratio <- lev_token_sort_ratio(df$expedia, df$booking) df$lev_token_set_ratio <- lev_token_set_ratio(df$expedia, df$booking)
We can write a function to return the best match from a list of candidates.
best_match <- function(a, b, FUN) { scores <- FUN(a = a, b = b) best <- order(scores, decreasing = TRUE)[1L] b[best] } best_match("cat", c("cot", "dog", "frog"), lev_ratio)
We can then use this to find out which of the Booking.com entries each of the functions choose for each of the Expedia entries.
best_match_by_fun <- function(FUN) { best_matches <- character(nrow(hotel_rooms)) for (i in seq_along(best_matches)) { best_matches[i] <- best_match(hotel_rooms$expedia[i], hotel_rooms$booking, FUN) } best_matches } df$lev_ratio_best_match <- best_match_by_fun(FUN = lev_ratio) df$lev_partial_ratio_best_match <- best_match_by_fun(FUN = lev_partial_ratio) df$lev_token_sort_ratio_best_match <- best_match_by_fun(FUN = lev_token_sort_ratio) df$lev_token_set_ratio_best_match <- best_match_by_fun(FUN = lev_token_set_ratio)
We can now see how many each algo got right.
message("`lev_ratio()`: ", sum(df$lev_ratio_best_match == df$booking) / nrow(df)) message("`lev_partial_ratio()`: ", sum(df$lev_partial_ratio_best_match == df$booking) / nrow(df)) message("`lev_token_sort_ratio()`: ", sum(df$lev_token_sort_ratio_best_match == df$booking) / nrow(df)) message("`lev_token_set_ratio()`: ", sum(df$lev_token_set_ratio_best_match == df$booking) / nrow(df))
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