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Hierarchical Classification

Source: vignettes/Hierarchical_classification.Rmd
Hierarchical_classification.Rmd

Data preparation

The supported data format for hierarchical classification is the Node object format from package data.tree.

This is a general purpose format that fits generic hierarchical tree encoding needs. Each node of the tree is associated with predictor values through the attributes in the data Node object.

  • A very basic example is the acme dataset to show you how the two predictors values cost and p are associates attributes of each node in the hierarchy :
data(acme, package = "data.tree")
acme$attributesAll
print(acme, "cost", "p" , limit = 8)

  • Multiple manual or programmatic methods are available to create or update predictors. They are detailled in the vignette("data.tree", package = "data.tree").

  • a lot of native hierarchical data-format conversion from files to Node are covered by thedata.tree package. You can find them in the “Create tree from a file” section of the same vignette. If needed, the ape package covers a lot of conversion format to the philo format. Thus you can reach the Node format in maybe two transformation steps…

  • A quick way to achieve Node format from a data frame with columns being the different levels of the hierarchy consist in pasting the columns into a single string with "/" separator into a pathString column. This will be turn into the expected hierarchy by the data.tree::as.Node() command.

Let’s do it with starwars dataset as a toy example :

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)

You may have noticed that name and height have unexpected values according to the original data: Human is not part of the name in orginal dataset, and height values have been changed into the local height of the tree. This is due to some rules we will have to follow to create the Node from data frame.

Node preparation rules for {tabnet} models

Avoid factor predictors

As as.Node() will only consider the as.numeric() values of a factor(), you should turn them into characters before applying the as.Node() function in order for {tabnet} to properly embed them.

Avoid column name collision with reserved {data.tree} names

name and height are both part of the NODE_RESERVED_NAMES_CONST reserved list of names for Node attributes. So they must not be used as predictor names, or the as.Node() function will silently discard them.

Avoid column named level_* to avoid collision with output data.tree names

Your dataset hierarchy will be turn internally into multi-outcomes named level_1 to level_n, n beeing the depth of your tree. Thus column names starting with level_ should be avoided.

Ensure the last hierarchy of the tree is the observation id

The tree only keeps a single row of attributes per tree leaf. Thus in order to transfer your complete predictors dataset into the Node object, you must keep the last level of the hierarchy to be a unique observation identifier (last resort beeing rowid_to_column() to achieve it).

The classification will be done removing the last level of hierarchy in any case.

Ensure there is a root level in the hierarchy

The tree should have a single root for all nodes to be consistent. Thus you have to use a constant prefix to all pathString.

The classification will be done removing the first level of hierarchy in any case.

Now let’s have all those rules applied to the starwars_tree :

# 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)

We can see that the reserved name column contains slightly different content that the original _name column.

Model building

Data set split

The starwars dataset contains list columns, hosting some variability in the predictor values. Thus we decide here to unnest_longer every list column to each of its values. This will triple the size of the starwars dataset.
The dataset split here will be done upfront of the transformation into as.Node(). We will use rsample::initial_split() to split with a stratification on the parent category of the first level of our hierarchy which is species.

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")

In order to train a model properly, we should prevent the outcomes to be part of the predictor columns. For the sake of demonstration, the _name column was present in starwars_tree but must now be dropped.

# 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

Now we can see that none of the predictor leaks the outcome hierarchy information.

Model building

This starwars_tree can now be used as an input for tabnet_fit() :

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)

Model diagnostic

We have avoid the verbose output of the model, thus very first diagnostic is the check for model over-fitting though the training loss plot.

autoplot(starw_model)

Then global feature importance gives us a clue of model quality

vip::vip(starw_model)

Model inference

We can infer on the test-set

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

We can see in the Warnings that the dataset is a challenge as many new levels are found in a lot of predictors in the test set.
The model also here is very poor on the level_2 ( species ) and on level_3 ( sex ) as this is definitively not a model-intended dataset. The reason is that the input dataset not collecting large samples of distinctive observation per leaf, but rather a very diverse but limited number of characters compatible with watching a movie saga.

Despite the performance, we do have local feature importance on the complete dataset here :

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

Hopefully your own hierarchical outcome will have a better success than the one here with starwars dataset. But in this journey, you have learned a lot in the data format constraints and solutions, and you now have a new performing solution in your toolbox.