create_xreg_recipe: Developer Tools for preparing XREGS (Regressors)

In modeltime: The Tidymodels Extension for Time Series Modeling

Developer Tools for preparing XREGS (Regressors)

Description

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 is FALSE.

Details

The default recipe contains steps to:

1. Remove date features

2. Clean the column names removing spaces and bad characters

3. Convert ordered factors to regular factors

4. Convert factors to dummy variables

5. Remove any variables that have zero variance

Value

A recipe in either prepared or un-prepared format.

Examples

library(dplyr)
library(timetk)
library(recipes)
library(lubridate)

predictors <- m4_monthly %>%
    filter(id == "M750") %>%
    select(-value) %>%
    mutate(month = month(date, label = TRUE))
predictors

# 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)

# Applies the prepared recipe to new data (used in your predict function)
bake_xreg_recipe(xreg_recipe_spec, new_data = predictors)

modeltime documentation built on Sept. 2, 2023, 5:06 p.m.