1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
|
#' Assign missing categories to "unknown"
#'
#' `step_unknown` creates a *specification* of a recipe
#' step that will assign a missing value in a factor level to"unknown".
#'
#' @inheritParams step_center
#' @param new_level A single character value that will be assigned
#' to new factor levels.
#' @param objects A list of objects that contain the information
#' on factor levels that will be determined by [prep()].
#' @template step-return
#' @family dummy variable and encoding steps
#' @seealso [dummy_names()]
#' @export
#' @details The selected variables are adjusted to have a new
#' level (given by `new_level`) that is placed in the last
#' position.
#'
#' Note that if the original columns are character, they will be
#' converted to factors by this step.
#'
#' If `new_level` is already in the data given to `prep`, an error
#' is thrown.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `terms` (the columns that will be affected) and `value` (the factor
#' levels that is used for the new value) is returned.
#'
#' @template case-weights-not-supported
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(Sacramento, package = "modeldata")
#'
#' rec <-
#' recipe(~ city + zip, data = Sacramento) %>%
#' step_unknown(city, new_level = "unknown city") %>%
#' step_unknown(zip, new_level = "unknown zip") %>%
#' prep()
#'
#' table(bake(rec, new_data = NULL) %>% pull(city),
#' Sacramento %>% pull(city),
#' useNA = "always"
#' ) %>%
#' as.data.frame() %>%
#' dplyr::filter(Freq > 0)
#'
#' tidy(rec, number = 1)
step_unknown <-
function(recipe,
...,
role = NA,
trained = FALSE,
new_level = "unknown",
objects = NULL,
skip = FALSE,
id = rand_id("unknown")) {
add_step(
recipe,
step_unknown_new(
terms = enquos(...),
role = role,
trained = trained,
new_level = new_level,
objects = objects,
skip = skip,
id = id
)
)
}
step_unknown_new <-
function(terms, role, trained, new_level, objects, skip, id) {
step(
subclass = "unknown",
terms = terms,
role = role,
trained = trained,
new_level = new_level,
objects = objects,
skip = skip,
id = id
)
}
#' @export
prep.step_unknown <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("string", "factor", "ordered"))
# Get existing levels and their factor type (i.e. ordered)
objects <- lapply(training[, col_names], get_existing_values)
# Check to make sure that there are not duplicate levels
level_check <-
map_lgl(objects, function(x, y) y %in% x, y = x$new_level)
if (any(level_check)) {
rlang::abort(
paste0(
"Columns already contain a level '", x$new_level, "': ",
paste0(names(level_check)[level_check], collapse = ", ")
)
)
}
step_unknown_new(
terms = x$terms,
role = x$role,
trained = TRUE,
new_level = x$new_level,
objects = objects,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_unknown <- function(object, new_data, ...) {
check_new_data(names(object$objects), object, new_data)
for (i in names(object$objects)) {
new_data[[i]] <-
ifelse(is.na(new_data[[i]]), object$new_level, as.character(new_data[[i]]))
new_levels <- c(object$object[[i]], object$new_level)
if (!all(new_data[[i]] %in% new_levels)) {
warn_new_levels(
new_data[[i]],
new_levels,
paste0(
"\nNew levels will be coerced to `NA` by `step_unknown()`.",
"\nConsider using `step_novel()` before `step_unknown()`."
)
)
}
new_data[[i]] <-
factor(new_data[[i]],
levels = new_levels,
ordered = attributes(object$object[[i]])$is_ordered
)
}
new_data
}
print.step_unknown <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Unknown factor level assignment for "
print_step(names(x$objects), x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_unknown <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(
terms = names(x$objects),
value = rep(x$new_level, length(x$objects))
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
value = rep(x$new_level, length(term_names))
)
}
res$id <- x$id
res
}
|
