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
|
#' Centering numeric data
#'
#' `step_center` creates a *specification* of a recipe
#' step that will normalize numeric data to have a mean of zero.
#'
#' @param recipe A recipe object. The step will be added to the
#' sequence of operations for this recipe.
#' @param ... One or more selector functions to choose variables
#' for this step. See [selections()] for more details.
#' @param role Not used by this step since no new variables are
#' created.
#' @param trained A logical to indicate if the quantities for
#' preprocessing have been estimated.
#' @param means A named numeric vector of means. This is
#' `NULL` until computed by [prep()].
#' @param na_rm A logical value indicating whether `NA`
#' values should be removed during computations.
#' @param skip A logical. Should the step be skipped when the
#' recipe is baked by [bake()]? While all operations are baked
#' when [prep()] is run, some operations may not be able to be
#' conducted on new data (e.g. processing the outcome variable(s)).
#' Care should be taken when using `skip = TRUE` as it may affect
#' the computations for subsequent operations.
#' @param id A character string that is unique to this step to identify it.
#' @template step-return
#'
#' @family normalization steps
#' @export
#' @details Centering data means that the average of a variable is
#' subtracted from the data. `step_center` estimates the
#' variable means from the data used in the `training`
#' argument of `prep.recipe`. `bake.recipe` then applies
#' the centering to new data sets using these means.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `terms` (the selectors or variables selected) and `value` (the means)
#' is returned.
#'
#' @template case-weights-unsupervised
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(biomass, package = "modeldata")
#'
#' biomass_tr <- biomass[biomass$dataset == "Training", ]
#' biomass_te <- biomass[biomass$dataset == "Testing", ]
#'
#' rec <- recipe(
#' HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
#' data = biomass_tr
#' )
#'
#' center_trans <- rec %>%
#' step_center(carbon, contains("gen"), -hydrogen)
#'
#' center_obj <- prep(center_trans, training = biomass_tr)
#'
#' transformed_te <- bake(center_obj, biomass_te)
#'
#' biomass_te[1:10, names(transformed_te)]
#' transformed_te
#'
#' tidy(center_trans, number = 1)
#' tidy(center_obj, number = 1)
step_center <-
function(recipe,
...,
role = NA,
trained = FALSE,
means = NULL,
na_rm = TRUE,
skip = FALSE,
id = rand_id("center")) {
add_step(
recipe,
step_center_new(
terms = enquos(...),
trained = trained,
role = role,
means = means,
na_rm = na_rm,
skip = skip,
id = id,
case_weights = NULL
)
)
}
## Initializes a new object
step_center_new <-
function(terms, role, trained, means, na_rm, skip, id, case_weights) {
step(
subclass = "center",
terms = terms,
role = role,
trained = trained,
means = means,
na_rm = na_rm,
skip = skip,
id = id,
case_weights = case_weights
)
}
#' @export
prep.step_center <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
wts <- get_case_weights(info, training)
were_weights_used <- are_weights_used(wts, unsupervised = TRUE)
if (isFALSE(were_weights_used)) {
wts <- NULL
}
means <- averages(training[, col_names], wts, na_rm = x$na_rm)
step_center_new(
terms = x$terms,
role = x$role,
trained = TRUE,
means = means,
na_rm = x$na_rm,
skip = x$skip,
id = x$id,
case_weights = were_weights_used
)
}
#' @export
bake.step_center <- function(object, new_data, ...) {
check_new_data(names(object$means), object, new_data)
for (column in names(object$means)) {
mean <- object$means[column]
new_data[[column]] <- new_data[[column]] - mean
}
new_data
}
print.step_center <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Centering for "
print_step(names(x$means), x$terms, x$trained, title, width,
case_weights = x$case_weights)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_center <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(
terms = names(x$means),
value = unname(x$means)
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
value = na_dbl
)
}
res$id <- x$id
res
}
|
