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
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
|
#' Generalized Bernstein Polynomial Basis
#'
#' `step_poly_bernstein` creates a *specification* of a recipe
#' step that creates Bernstein polynomial features.
#'
#' @inheritParams step_spline_b
#' @param degree The degrees of the polynomial. As the degrees for a polynomial
#' increase, more flexible and complex curves can be generated.
#' @param options A list of options for [splines2::bernsteinPoly()]
#' which should not include `x` or `degree`.
#' @return An object with classes `"step_poly_bernstein"` and `"step"`.
#' @export
#' @details
#'
#' Polynomial transformations take a numeric column and create multiple features
#' that, when used in a model, can estimate nonlinear trends between the column
#' and some outcome. The degrees of freedom determines how many new features
#' are added to the data.
#'
#' If the spline expansion fails for a selected column, the step will
#' remove that column's results (but will retain the original data). Use the
#' `tidy()` method to determine which columns were used.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with column
#' `terms` (the columns that will be affected) is returned.
#'
#' @examplesIf rlang::is_installed(c("modeldata", "ggplot2"))
#' library(tidyr)
#' library(dplyr)
#'
#' library(ggplot2)
#' data(ames, package = "modeldata")
#'
#' spline_rec <- recipe(Sale_Price ~ Longitude, data = ames) %>%
#' step_poly_bernstein(Longitude, degree = 6, keep_original_cols = TRUE) %>%
#' prep()
#'
#' tidy(spline_rec, number = 1)
#'
#' # Show where each feature is active
#' spline_rec %>%
#' bake(new_data = NULL,-Sale_Price) %>%
#' pivot_longer(c(starts_with("Longitude_")), names_to = "feature", values_to = "value") %>%
#' mutate(feature = gsub("Longitude_", "feature ", feature)) %>%
#' filter(value > 0) %>%
#' ggplot(aes(x = Longitude, y = value)) +
#' geom_line() +
#' facet_wrap(~ feature)
#' @template case-weights-not-supported
#' @seealso [splines2::bernsteinPoly()]
step_poly_bernstein <-
function(recipe,
...,
role = NA,
trained = FALSE,
degree = 10,
complete_set = FALSE,
options = NULL,
keep_original_cols = FALSE,
results = NULL,
skip = FALSE,
id = rand_id("poly_bernstein")) {
recipes_pkg_check(required_pkgs.step_poly_bernstein())
add_step(
recipe,
step_poly_bernstein_new(
terms = enquos(...),
trained = trained,
role = role,
degree = degree,
complete_set = complete_set,
options = options,
keep_original_cols = keep_original_cols,
results = results,
skip = skip,
id = id
)
)
}
step_poly_bernstein_new <-
function(terms, trained, role, degree, complete_set, options, keep_original_cols,
results, na_rm, skip, id) {
step(
subclass = "poly_bernstein",
terms = terms,
role = role,
trained = trained,
degree = degree,
complete_set = complete_set,
options = options,
keep_original_cols = keep_original_cols,
results = results,
skip = skip,
id = id
)
}
# ------------------------------------------------------------------------------
prep.step_poly_bernstein <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
x$options <- c(x$options, degree = x$degree)
res <-
purrr::map2(
training[, col_names],
col_names,
~ spline2_create(
.x,
nm = .y,
.fn = "bernsteinPoly",
df = NULL,
complete_set = x$complete_set,
fn_opts = x$options
)
)
# check for errors
bas_res <- purrr::map_lgl(res, is.null)
res <- res[!bas_res]
col_names <- col_names[!bas_res]
names(res) <- col_names
step_poly_bernstein_new(
terms = x$terms,
role = x$role,
trained = TRUE,
degree = x$degree,
complete_set = x$complete_set,
options = x$options,
keep_original_cols = x$keep_original_cols,
results = res,
skip = x$skip,
id = x$id
)
}
bake.step_poly_bernstein <- function(object, new_data, ...) {
orig_names <- names(object$results)
if (length(orig_names) > 0) {
new_cols <- purrr::map2_dfc(object$results, new_data[, orig_names], spline2_apply)
new_data <- bind_cols(new_data, new_cols)
keep_original_cols <- get_keep_original_cols(object)
if (!keep_original_cols) {
new_data <- new_data[, !(colnames(new_data) %in% orig_names), drop = FALSE]
}
}
as_tibble(new_data)
}
# ------------------------------------------------------------------------------
print.step_poly_bernstein <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Bernstein polynomial expansion "
cols_used <- names(x$results)
if (length(cols_used) == 0) {
cols_used <- "<none>"
}
print_step(cols_used, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_poly_bernstein <- function(x, ...) {
if (is_trained(x)) {
terms <- names(x$results)
if (length(terms) == 0) {
terms <- "<none>"
}
} else {
terms <- sel2char(x$terms)
}
tibble(terms = terms, id = x$id)
}
# ------------------------------------------------------------------------------
#' @rdname required_pkgs.recipe
#' @export
required_pkgs.step_poly_bernstein <- function(x, ...) {
c("splines2")
}
# ------------------------------------------------------------------------------
#' @export
tunable.step_poly_bernstein <- function(x, ...) {
tibble::tibble(
name = c("degree"),
call_info = list(
list(pkg = "dials", fun = "degree_int", range = c(1L, 15L))
),
source = "recipe",
component = "step_poly_bernstein",
component_id = x$id
)
}
|
