library(textrecipes)
#> Loading required package: recipes
#> Loading required package: dplyr
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
#>
#> Attaching package: 'recipes'
#> The following object is masked from 'package:stats':
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#> step
library(tokenizers)If you want to use n-grams with textrecipes you have 2 options:
step_tokenize() that tokenizes to
n-grams.step_tokenize() and use
step_ngram() to turn them into n-grams.Both of these methods come with pros and cons so it will be worthwhile for you to be aware of both.
before we get started let’s make sure we are on the same page of what
we mean when we are talking about n-grams. We normally tokenize our text
into words, which we can do with tokenize_words() from the
tokenizers package (this is the default engine and token for
step_tokenize() in textrecipes)
abc <- c("The Bank is a place where you put your money;",
"The Bee is an insect that gathers honey.")
tokenize_words(abc)
#> [[1]]
#> [1] "the" "bank" "is" "a" "place" "where" "you" "put" "your"
#> [10] "money"
#>
#> [[2]]
#> [1] "the" "bee" "is" "an" "insect" "that" "gathers"
#> [8] "honey"N-grams are a contiguous sequence of n tokens. So to get 2-gram (or
bigrams as they are also called) we can use the
tokenize_ngrams() function to get them
tokenize_ngrams(abc, n = 2)
#> [[1]]
#> [1] "the bank" "bank is" "is a" "a place" "place where"
#> [6] "where you" "you put" "put your" "your money"
#>
#> [[2]]
#> [1] "the bee" "bee is" "is an" "an insect"
#> [5] "insect that" "that gathers" "gathers honey"Notice how the words appear in multiple n-grams as the window slides
across them. And why changing the n argument we can any
kind of n-gram (notice how n = 1 is the special case of
tokenizing to words).
tokenize_ngrams(abc, n = 3)
#> [[1]]
#> [1] "the bank is" "bank is a" "is a place" "a place where"
#> [5] "place where you" "where you put" "you put your" "put your money"
#>
#> [[2]]
#> [1] "the bee is" "bee is an" "is an insect"
#> [4] "an insect that" "insect that gathers" "that gathers honey"
tokenize_ngrams(abc, n = 1)
#> [[1]]
#> [1] "the" "bank" "is" "a" "place" "where" "you" "put" "your"
#> [10] "money"
#>
#> [[2]]
#> [1] "the" "bee" "is" "an" "insect" "that" "gathers"
#> [8] "honey"It can also be beneficial to specify a delimiter between the tokens in your n-gram.
tokenize_ngrams(abc, n = 3, ngram_delim = "_")
#> [[1]]
#> [1] "the_bank_is" "bank_is_a" "is_a_place" "a_place_where"
#> [5] "place_where_you" "where_you_put" "you_put_your" "put_your_money"
#>
#> [[2]]
#> [1] "the_bee_is" "bee_is_an" "is_an_insect"
#> [4] "an_insect_that" "insect_that_gathers" "that_gathers_honey"step_tokenize()The first methods work by using n-gram token from one of
the built-in engine in step_tokenize() to get a full list
of available tokens type ?step_tokenize() and go down to
Details. We can use the token="ngrams" along
with engine = "tokenizers"(the default) to tokenize to
n-grams. We finish this recipe() with
step_tokenfilter() and step_tf(). The
filtering doesn’t do anything to the data of this size but it is a good
practice to use step_tokenfilter() before using
step_tf() or step_tfidf() to control the size
of the resulting data.frame.
abc_tibble <- tibble(text = abc)
rec <- recipe(~ text, data = abc_tibble) %>%
step_tokenize(text, token = "ngrams") %>%
step_tokenfilter(text) %>%
step_tf(text)
abc_ngram <- rec %>%
prep() %>%
juice()
#> Warning: max_tokens was set to '100', but only 14 was available and selected.
abc_ngram
#> # A tibble: 2 × 14
#> tf_t…¹ tf_…² tf_…³ tf_…⁴ tf_…⁵ tf_…⁶ tf_…⁷ tf_…⁸ tf_…⁹ tf_…˟ tf_…˟ tf_…˟ tf_…˟
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 1 0 0 1 0 1 1 0 1 0 1
#> 2 0 1 0 1 1 0 1 0 0 1 0 1 0
#> # … with abbreviated variable names ¹`tf_text_a place where`,
#> # ²`tf_text_an insect that`, ³`tf_text_bank is a`, ⁴`tf_text_bee is an`,
#> # ⁵`tf_text_insect that gathers`, ⁶`tf_text_is a place`,
#> # ⁷`tf_text_is an insect`, ⁸`tf_text_place where you`,
#> # ⁹`tf_text_put your money`, ˟`tf_text_that gathers honey`,
#> # ˟`tf_text_the bank is`, ˟`tf_text_the bee is`, ˟`tf_text_where you put`,
#> # and 1 more variable: `tf_text_you put your` <dbl>
names(abc_ngram)
#> [1] "tf_text_a place where" "tf_text_an insect that"
#> [3] "tf_text_bank is a" "tf_text_bee is an"
#> [5] "tf_text_insect that gathers" "tf_text_is a place"
#> [7] "tf_text_is an insect" "tf_text_place where you"
#> [9] "tf_text_put your money" "tf_text_that gathers honey"
#> [11] "tf_text_the bank is" "tf_text_the bee is"
#> [13] "tf_text_where you put" "tf_text_you put your"If you need to pass arguments to the underlying tokenizer function
you can pass a named list to the options argument in
step_tokenize()
abc_tibble <- tibble(text = abc)
rec <- recipe(~ text, data = abc_tibble) %>%
step_tokenize(text, token = "ngrams", options = list(n = 2,
ngram_delim = "_")) %>%
step_tokenfilter(text) %>%
step_tf(text)
abc_ngram <- rec %>%
prep() %>%
juice()
#> Warning: max_tokens was set to '100', but only 16 was available and selected.
abc_ngram
#> # A tibble: 2 × 16
#> tf_t…¹ tf_…² tf_…³ tf_…⁴ tf_…⁵ tf_…⁶ tf_…⁷ tf_…⁸ tf_…⁹ tf_…˟ tf_…˟ tf_…˟ tf_…˟
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 1 0 0 0 1 0 1 1 0 1 0
#> 2 0 1 0 1 1 1 0 1 0 0 1 0 1
#> # … with abbreviated variable names ¹tf_text_a_place, ²tf_text_an_insect,
#> # ³tf_text_bank_is, ⁴tf_text_bee_is, ⁵tf_text_gathers_honey,
#> # ⁶tf_text_insect_that, ⁷tf_text_is_a, ⁸tf_text_is_an, ⁹tf_text_place_where,
#> # ˟tf_text_put_your, ˟tf_text_that_gathers, ˟tf_text_the_bank,
#> # ˟tf_text_the_bee, and 3 more variables: tf_text_where_you <dbl>,
#> # tf_text_you_put <dbl>, tf_text_your_money <dbl>
names(abc_ngram)
#> [1] "tf_text_a_place" "tf_text_an_insect" "tf_text_bank_is"
#> [4] "tf_text_bee_is" "tf_text_gathers_honey" "tf_text_insect_that"
#> [7] "tf_text_is_a" "tf_text_is_an" "tf_text_place_where"
#> [10] "tf_text_put_your" "tf_text_that_gathers" "tf_text_the_bank"
#> [13] "tf_text_the_bee" "tf_text_where_you" "tf_text_you_put"
#> [16] "tf_text_your_money"Lastly you can also supply a custom tokenizer to
step_tokenize() using the custom_token
argument.
abc_tibble <- tibble(text = abc)
bigram <- function(x) {
tokenizers::tokenize_ngrams(x, lowercase = FALSE, n = 2, ngram_delim = ".")
}
rec <- recipe(~ text, data = abc_tibble) %>%
step_tokenize(text, custom_token = bigram) %>%
step_tokenfilter(text) %>%
step_tf(text)
abc_ngram <- rec %>%
prep() %>%
juice()
#> Warning: max_tokens was set to '100', but only 16 was available and selected.
abc_ngram
#> # A tibble: 2 × 16
#> tf_t…¹ tf_…² tf_…³ tf_…⁴ tf_…⁵ tf_…⁶ tf_…⁷ tf_…⁸ tf_…⁹ tf_…˟ tf_…˟ tf_…˟ tf_…˟
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 1 0 1 0 0 0 1 0 1 1 0
#> 2 0 1 0 1 0 1 1 1 0 1 0 0 1
#> # … with abbreviated variable names ¹tf_text_Bank.is, ²tf_text_Bee.is,
#> # ³tf_text_The.Bank, ⁴tf_text_The.Bee, ⁵tf_text_a.place, ⁶tf_text_an.insect,
#> # ⁷tf_text_gathers.honey, ⁸tf_text_insect.that, ⁹tf_text_is.a,
#> # ˟tf_text_is.an, ˟tf_text_place.where, ˟tf_text_put.your,
#> # ˟tf_text_that.gathers, and 3 more variables: tf_text_where.you <dbl>,
#> # tf_text_you.put <dbl>, tf_text_your.money <dbl>
names(abc_ngram)
#> [1] "tf_text_Bank.is" "tf_text_Bee.is" "tf_text_The.Bank"
#> [4] "tf_text_The.Bee" "tf_text_a.place" "tf_text_an.insect"
#> [7] "tf_text_gathers.honey" "tf_text_insect.that" "tf_text_is.a"
#> [10] "tf_text_is.an" "tf_text_place.where" "tf_text_put.your"
#> [13] "tf_text_that.gathers" "tf_text_where.you" "tf_text_you.put"
#> [16] "tf_text_your.money"Pros:
Cons:
tokenizers::tokenize_ngrams()
don’t let you control how the words are tokenized.)step_tokenize() and
step_ngram()As of version 0.2.0 you can use step_ngram() along with
step_tokenize() to gain higher control over how your
n-grams are being generated.
abc_tibble <- tibble(text = abc)
rec <- recipe(~ text, data = abc_tibble) %>%
step_tokenize(text) %>%
step_ngram(text, num_tokens = 3) %>%
step_tokenfilter(text) %>%
step_tf(text)
abc_ngram <- rec %>%
prep() %>%
juice()
#> Warning: max_tokens was set to '100', but only 14 was available and selected.
abc_ngram
#> # A tibble: 2 × 14
#> tf_t…¹ tf_…² tf_…³ tf_…⁴ tf_…⁵ tf_…⁶ tf_…⁷ tf_…⁸ tf_…⁹ tf_…˟ tf_…˟ tf_…˟ tf_…˟
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 1 0 0 1 0 1 1 0 1 0 1
#> 2 0 1 0 1 1 0 1 0 0 1 0 1 0
#> # … with abbreviated variable names ¹tf_text_a_place_where,
#> # ²tf_text_an_insect_that, ³tf_text_bank_is_a, ⁴tf_text_bee_is_an,
#> # ⁵tf_text_insect_that_gathers, ⁶tf_text_is_a_place, ⁷tf_text_is_an_insect,
#> # ⁸tf_text_place_where_you, ⁹tf_text_put_your_money,
#> # ˟tf_text_that_gathers_honey, ˟tf_text_the_bank_is, ˟tf_text_the_bee_is,
#> # ˟tf_text_where_you_put, and 1 more variable: tf_text_you_put_your <dbl>
names(abc_ngram)
#> [1] "tf_text_a_place_where" "tf_text_an_insect_that"
#> [3] "tf_text_bank_is_a" "tf_text_bee_is_an"
#> [5] "tf_text_insect_that_gathers" "tf_text_is_a_place"
#> [7] "tf_text_is_an_insect" "tf_text_place_where_you"
#> [9] "tf_text_put_your_money" "tf_text_that_gathers_honey"
#> [11] "tf_text_the_bank_is" "tf_text_the_bee_is"
#> [13] "tf_text_where_you_put" "tf_text_you_put_your"Now you are able to perform additional steps between the tokenization and the n-gram creation such as stemming the tokens.
abc_tibble <- tibble(text = abc)
rec <- recipe(~ text, data = abc_tibble) %>%
step_tokenize(text) %>%
step_stem(text) %>%
step_ngram(text, num_tokens = 3) %>%
step_tokenfilter(text) %>%
step_tf(text)
abc_ngram <- rec %>%
prep() %>%
juice()
#> Warning: max_tokens was set to '100', but only 14 was available and selected.
abc_ngram
#> # A tibble: 2 × 14
#> tf_t…¹ tf_…² tf_…³ tf_…⁴ tf_…⁵ tf_…⁶ tf_…⁷ tf_…⁸ tf_…⁹ tf_…˟ tf_…˟ tf_…˟ tf_…˟
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 1 0 1 0 0 1 1 0 1 0 1
#> 2 0 1 0 1 0 1 1 0 0 1 0 1 0
#> # … with abbreviated variable names ¹tf_text_a_place_where,
#> # ²tf_text_an_insect_that, ³tf_text_bank_i_a, ⁴tf_text_bee_i_an,
#> # ⁵tf_text_i_a_place, ⁶tf_text_i_an_insect, ⁷tf_text_insect_that_gather,
#> # ⁸tf_text_place_where_you, ⁹tf_text_put_your_monei,
#> # ˟tf_text_that_gather_honei, ˟tf_text_the_bank_i, ˟tf_text_the_bee_i,
#> # ˟tf_text_where_you_put, and 1 more variable: tf_text_you_put_your <dbl>
names(abc_ngram)
#> [1] "tf_text_a_place_where" "tf_text_an_insect_that"
#> [3] "tf_text_bank_i_a" "tf_text_bee_i_an"
#> [5] "tf_text_i_a_place" "tf_text_i_an_insect"
#> [7] "tf_text_insect_that_gather" "tf_text_place_where_you"
#> [9] "tf_text_put_your_monei" "tf_text_that_gather_honei"
#> [11] "tf_text_the_bank_i" "tf_text_the_bee_i"
#> [13] "tf_text_where_you_put" "tf_text_you_put_your"This also works great for cases where you need higher flexibility or when you want to use a more powerful engine such as spacyr that doesn’t come with an n-gram tokenizer.
Furthermore the num_tokens argument is tunable with the
dials and tune package.
Pros:
Cons: