These functions are designed to assist developers in extending the modeltime
package. create_xregs_recipe() makes it simple to automate conversion
of raw un-encoded features to machine-learning ready features.
Usage
create_xreg_recipe(
data,
prepare = TRUE,
clean_names = TRUE,
dummy_encode = TRUE,
one_hot = FALSE
)Arguments
- data
A data frame
- prepare
Whether or not to run
recipes::prep()on the final recipe. Default is to prepare. User can set this to FALSE to return an un prepared recipe.- clean_names
Uses
janitor::clean_names()to process the names and improve robustness to failure during dummy (one-hot) encoding step.- dummy_encode
Should
factors(categorical data) be- one_hot
If
dummy_encode = TRUE, should the encoding return one column for each feature or one less column than each feature. Default isFALSE.
Details
The default recipe contains steps to:
Remove date features
Clean the column names removing spaces and bad characters
Convert ordered factors to regular factors
Convert factors to dummy variables
Remove any variables that have zero variance
Examples
library(dplyr)
library(timetk)
library(recipes)
library(lubridate)
predictors <- m4_monthly %>%
filter(id == "M750") %>%
select(-value) %>%
mutate(month = month(date, label = TRUE))
predictors
#> # A tibble: 306 × 3
#> id date month
#> <fct> <date> <ord>
#> 1 M750 1990-01-01 Jan
#> 2 M750 1990-02-01 Feb
#> 3 M750 1990-03-01 Mar
#> 4 M750 1990-04-01 Apr
#> 5 M750 1990-05-01 May
#> 6 M750 1990-06-01 Jun
#> 7 M750 1990-07-01 Jul
#> 8 M750 1990-08-01 Aug
#> 9 M750 1990-09-01 Sep
#> 10 M750 1990-10-01 Oct
#> # ℹ 296 more rows
# Create default recipe
xreg_recipe_spec <- create_xreg_recipe(predictors, prepare = TRUE)
# Extracts the preprocessed training data from the recipe (used in your fit function)
juice_xreg_recipe(xreg_recipe_spec)
#> # A tibble: 306 × 11
#> month_Feb month_Mar month_Apr month_May month_Jun month_Jul month_Aug
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0 0 0
#> 2 1 0 0 0 0 0 0
#> 3 0 1 0 0 0 0 0
#> 4 0 0 1 0 0 0 0
#> 5 0 0 0 1 0 0 0
#> 6 0 0 0 0 1 0 0
#> 7 0 0 0 0 0 1 0
#> 8 0 0 0 0 0 0 1
#> 9 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0
#> # ℹ 296 more rows
#> # ℹ 4 more variables: month_Sep <dbl>, month_Oct <dbl>, month_Nov <dbl>,
#> # month_Dec <dbl>
# Applies the prepared recipe to new data (used in your predict function)
bake_xreg_recipe(xreg_recipe_spec, new_data = predictors)
#> # A tibble: 306 × 11
#> month_Feb month_Mar month_Apr month_May month_Jun month_Jul month_Aug
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0 0 0
#> 2 1 0 0 0 0 0 0
#> 3 0 1 0 0 0 0 0
#> 4 0 0 1 0 0 0 0
#> 5 0 0 0 1 0 0 0
#> 6 0 0 0 0 1 0 0
#> 7 0 0 0 0 0 1 0
#> 8 0 0 0 0 0 0 1
#> 9 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0
#> # ℹ 296 more rows
#> # ℹ 4 more variables: month_Sep <dbl>, month_Oct <dbl>, month_Nov <dbl>,
#> # month_Dec <dbl>