The package supplies two pre-generated workflow sets, two_class_set
and chi_features_set
, and associated sets of model fits
two_class_res
and chi_features_res
.
The two_class_*
objects are based on a binary classification problem
using the two_class_dat
data from the modeldata package. The six
models utilize either a bare formula or a basic recipe utilizing
recipes::step_YeoJohnson()
as a preprocessor, and a decision tree,
logistic regression, or MARS model specification. See ?two_class_set
for source code.
The chi_features_*
objects are based on a regression problem using the
Chicago
data from the modeldata package. Each of the three models
utilize a linear regression model specification, with three different
recipes of varying complexity. The objects are meant to approximate the
sequence of models built in Section 1.3 of Kuhn and Johnson (2019). See
?chi_features_set
for source code.
Details
See below for the source code to generate the Chicago Features example workflow sets:
library(workflowsets)
library(workflows)
library(modeldata)
library(recipes)
library(parsnip)
library(dplyr)
library(rsample)
library(tune)
library(yardstick)
library(dials)
# ------------------------------------------------------------------------------
# Slightly smaller data size
data(Chicago)
Chicago <- Chicago[1:1195,]
time_val_split <-
sliding_period(
Chicago,
date,
"month",
lookback = 38,
assess_stop = 1
)
# ------------------------------------------------------------------------------
base_recipe <-
recipe(ridership ~ ., data = Chicago) %>%
# create date features
step_date(date) %>%
step_holiday(date) %>%
# remove date from the list of predictors
update_role(date, new_role = "id") %>%
# create dummy variables from factor columns
step_dummy(all_nominal()) %>%
# remove any columns with a single unique value
step_zv(all_predictors()) %>%
step_normalize(all_predictors())
date_only <-
recipe(ridership ~ ., data = Chicago) %>%
# create date features
step_date(date) %>%
update_role(date, new_role = "id") %>%
# create dummy variables from factor columns
step_dummy(all_nominal()) %>%
# remove any columns with a single unique value
step_zv(all_predictors())
date_and_holidays <-
recipe(ridership ~ ., data = Chicago) %>%
# create date features
step_date(date) %>%
step_holiday(date) %>%
# remove date from the list of predictors
update_role(date, new_role = "id") %>%
# create dummy variables from factor columns
step_dummy(all_nominal()) %>%
# remove any columns with a single unique value
step_zv(all_predictors())
date_and_holidays_and_pca <-
recipe(ridership ~ ., data = Chicago) %>%
# create date features
step_date(date) %>%
step_holiday(date) %>%
# remove date from the list of predictors
update_role(date, new_role = "id") %>%
# create dummy variables from factor columns
step_dummy(all_nominal()) %>%
# remove any columns with a single unique value
step_zv(all_predictors()) %>%
step_pca(!!stations, num_comp = tune())
# ------------------------------------------------------------------------------
lm_spec <- linear_reg() %>% set_engine("lm")
# ------------------------------------------------------------------------------
pca_param <-
parameters(num_comp()) %>%
update(num_comp = num_comp(c(0, 20)))
# ------------------------------------------------------------------------------
chi_features_set <-
workflow_set(
preproc = list(date = date_only,
plus_holidays = date_and_holidays,
plus_pca = date_and_holidays_and_pca),
models = list(lm = lm_spec),
cross = TRUE
)
# ------------------------------------------------------------------------------
chi_features_res <-
chi_features_set %>%
option_add(param_info = pca_param, id = "plus_pca_lm") %>%
workflow_map(resamples = time_val_split, grid = 21, seed = 1, verbose = TRUE)
References
Max Kuhn and Kjell Johnson (2019) Feature Engineering and Selection, https://bookdown.org/max/FES/a-more-complex-example.html
Examples
data(chi_features_set)
chi_features_set
#> # A workflow set/tibble: 3 × 4
#> wflow_id info option result
#> <chr> <list> <list> <list>
#> 1 date_lm <tibble [1 × 4]> <opts[0]> <list [0]>
#> 2 plus_holidays_lm <tibble [1 × 4]> <opts[0]> <list [0]>
#> 3 plus_pca_lm <tibble [1 × 4]> <opts[0]> <list [0]>