chi_features_set: Chicago Features Example Data

In workflowsets: Create a Collection of 'tidymodels' Workflows

Chicago Features Example Data

Description

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

workflowsets documentation built on April 7, 2023, 1:05 a.m.