Recipes Step Fourier Discrete Generator

step_hai_fourier_discrete creates a a specification of a recipe step that will convert numeric data into either a 'sin', 'cos', or 'sincos' feature that can aid in machine learning.

Usage

step_hai_fourier_discrete(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  scale_type = c("sin", "cos", "sincos"),
  period = 1,
  order = 1,
  skip = FALSE,
  id = rand_id("hai_fourier_discrete")
)

Arguments

recipe: A recipe object. The step will be added to the sequence of operations for this recipe.
...: One or more selector functions to choose which variables that will be used to create the new variables. The selected variables should have class numeric or date,POSIXct
role: For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new variable columns created by the original variables will be used as predictors in a model.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
columns: A character string of variables that will be used as inputs. This field is a placeholder and will be populated once recipes::prep() is used.
scale_type: A character string of a scaling type, one of "sin","cos", or "sincos"
period: The number of observations that complete a cycle
order: The fourier term order
skip: A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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.
id: A character string that is unique to this step to identify it.

Value

For step_hai_fourier_discrete, an updated version of recipe with the new step added to the sequence of existing steps (if any).

Main Recipe Functions:

Details

Numeric Variables Unlike other steps, step_hai_fourier_discrete does not remove the original numeric variables. recipes::step_rm() can be used for this purpose.

Examples

suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(recipes))

len_out <- 10
by_unit <- "month"
start_date <- as.Date("2021-01-01")

data_tbl <- tibble(
  date_col = seq.Date(from = start_date, length.out = len_out, by = by_unit),
  a = rnorm(len_out),
  b = runif(len_out)
)

# Create a recipe object
rec_obj <- recipe(a ~ ., data = data_tbl) %>%
  step_hai_fourier_discrete(b, scale_type = "sin") %>%
  step_hai_fourier_discrete(b, scale_type = "cos") %>%
  step_hai_fourier_discrete(b, scale_type = "sincos")

# View the recipe object
rec_obj
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 2
#> 
#> ── Operations 
#> • Fourier Discrete Transformation on: b
#> • Fourier Discrete Transformation on: b
#> • Fourier Discrete Transformation on: b

# Prepare the recipe object
prep(rec_obj)
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 2
#> 
#> ── Training information 
#> Training data contained 10 data points and no incomplete rows.
#> 
#> ── Operations 
#> • Fourier Discrete Transformation on: b | Trained
#> • Fourier Discrete Transformation on: b | Trained
#> • Fourier Discrete Transformation on: b | Trained

# Bake the recipe object - Adds the Time Series Signature
bake(prep(rec_obj), data_tbl)
#> # A tibble: 10 × 6
#>    date_col        b       a fourier_discrete_b_sin fourier_discrete_b_cos
#>    <date>      <dbl>   <dbl>                  <dbl>                  <dbl>
#>  1 2021-01-01 0.0485  1.74                        1                      1
#>  2 2021-02-01 0.543   0.0260                      0                      0
#>  3 2021-03-01 0.424  -0.354                       1                      0
#>  4 2021-04-01 0.865  -0.617                       0                      1
#>  5 2021-05-01 0.493  -0.859                       1                      0
#>  6 2021-06-01 0.0304 -0.198                       1                      1
#>  7 2021-07-01 0.653  -1.65                        0                      0
#>  8 2021-08-01 0.552   1.57                        0                      0
#>  9 2021-09-01 0.745   0.576                       0                      0
#> 10 2021-10-01 0.818  -2.06                        0                      1
#> # ℹ 1 more variable: fourier_discrete_b_sincos <dbl>

rec_obj %>% get_juiced_data()
#> # A tibble: 10 × 6
#>    date_col        b       a fourier_discrete_b_sin fourier_discrete_b_cos
#>    <date>      <dbl>   <dbl>                  <dbl>                  <dbl>
#>  1 2021-01-01 0.0485  1.74                        1                      1
#>  2 2021-02-01 0.543   0.0260                      0                      0
#>  3 2021-03-01 0.424  -0.354                       1                      0
#>  4 2021-04-01 0.865  -0.617                       0                      1
#>  5 2021-05-01 0.493  -0.859                       1                      0
#>  6 2021-06-01 0.0304 -0.198                       1                      1
#>  7 2021-07-01 0.653  -1.65                        0                      0
#>  8 2021-08-01 0.552   1.57                        0                      0
#>  9 2021-09-01 0.745   0.576                       0                      0
#> 10 2021-10-01 0.818  -2.06                        0                      1
#> # ℹ 1 more variable: fourier_discrete_b_sincos <dbl>

Usage

Arguments

Value

Details

See also

Examples