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)
#> expedia
#> 1 Standard Room, 1 King Bed, Accessible
#> 2 Grand Corner King Room, 1 King Bed
#> 3 Suite, 1 King Bed (Parlor)
#> 4 High-Floor Premium Room, 1 King Bed
#> 5 Room, 1 King Bed, Accessible
#> 6 Room, 2 Double Beds (19th to 25th Floors)
#> booking
#> 1 Standard King Roll-in Shower Accessible
#> 2 Grand Corner King Room
#> 3 King Parlor Suite
#> 4 High-Floor Premium King Room
#> 5 King Room - Disability Access
#> 6 Two Double Beds - Location Room (19th to 25th Floors)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)
#> [1] "cot"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))
#> `lev_ratio()`: 0.329411764705882
message("`lev_partial_ratio()`: ", sum(df$lev_partial_ratio_best_match == df$booking) / nrow(df))
#> `lev_partial_ratio()`: 0.223529411764706
message("`lev_token_sort_ratio()`: ", sum(df$lev_token_sort_ratio_best_match == df$booking) / nrow(df))
#> `lev_token_sort_ratio()`: 0.564705882352941
message("`lev_token_set_ratio()`: ", sum(df$lev_token_set_ratio_best_match == df$booking) / nrow(df))
#> `lev_token_set_ratio()`: 0.376470588235294