## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----setup--------------------------------------------------------------------
# library(tabnet)
# library(dplyr)
# library(data.tree)
# library(ggplot2)
# library(rsample)
# library(tibble)
# set.seed(202307)

## -----------------------------------------------------------------------------
# data(acme, package = "data.tree")
# acme$attributesAll
# print(acme, "cost", "p" , limit = 8)

## -----------------------------------------------------------------------------
# data(starwars, package = "dplyr")
# head(starwars, 4)
# 
# # erroneous Node construction
# starwars_tree <- starwars %>%
#   mutate(pathString = paste("StarWars_characters", species, sex, `name`, sep = "/")) %>%
#   as.Node()
# print(starwars_tree, "name","height", "mass", "eye_color", limit = 8)
# 

## -----------------------------------------------------------------------------
# # demonstration of reserved column modification in Node construction
# starwars_tree <- starwars %>%
#   rename(`_name` = "name", `_height` = "height") %>%
#   mutate(pathString = paste("StarWars_characters", species, sex, `_name`, sep = "/")) %>%
#   as.Node()
# print(starwars_tree, "name", "_name","_height", "mass", "eye_color", limit = 8)

## -----------------------------------------------------------------------------
# starw_split <- starwars %>%
#   tidyr::unnest_longer(films) %>%
#   tidyr::unnest_longer(vehicles, keep_empty = TRUE) %>%
#   tidyr::unnest_longer(starships, keep_empty = TRUE) %>%
#   initial_split( prop = .8, strata = "species")

## -----------------------------------------------------------------------------
# # correct Node construction for hierarchical modeling
# starwars_train_tree <- starw_split %>%
#   training() %>%
#   # avoid reserved column names
#   rename(`_name` = "name", `_height` = "height") %>%
#   rowid_to_column() %>%
#   mutate(pathString = paste("StarWars_characters", species, sex, rowid, sep = "/")) %>%
#   # remove outcomes labels from predictors
#   select(-species, -sex, -`_name`, -rowid) %>%
#   # turn it as hierarchical Node
#   as.Node()
# 
# starwars_test_tree <- starw_split %>%
#   testing() %>%
#   rename(`_name` = "name", `_height` = "height") %>%
#   rowid_to_column() %>%
#   mutate(pathString = paste("StarWars_characters", species, sex, rowid, sep = "/")) %>%
#   select(-species, -sex, -`_name`, -rowid) %>%
#   as.Node()
# 
# starwars_train_tree$attributesAll

## ----model training-----------------------------------------------------------
# config <- tabnet_config(decision_width = 8, attention_width = 8, num_steps = 3, penalty = .003, cat_emb_dim = 2, valid_split = 0.2, learn_rate = 1e-3, lr_scheduler = "reduce_on_plateau", early_stopping_monitor = "valid_loss", early_stopping_patience = 4, verbose = FALSE)
# 
# starw_model <- tabnet_fit(starwars_train_tree, config = config, epoch = 170, checkpoint_epochs = 25)
# 

## -----------------------------------------------------------------------------
# autoplot(starw_model)

## -----------------------------------------------------------------------------
# vip::vip(starw_model)

## -----------------------------------------------------------------------------
# starwars_hat <- bind_cols(
#     predict(starw_model, starwars_test_tree),
#     node_to_df(starwars_test_tree)$y
#   )
# tail(starwars_hat, n = 5)
# 

## -----------------------------------------------------------------------------
# starwars_explain <- tabnet_explain(starw_model, starwars_test_tree)
# autoplot(starwars_explain)
# autoplot(starwars_explain, type = "steps")