Plot a tree family

Illustrates how a Random Planted Forest predicts by drawing the trees of one tree family. Each tree belongs to a set of variables, and its leaves are boxes in those variables with a value each. The prediction for a point is the root value plus the values of all leaves containing it, averaged over tree families.

Usage

# S3 method for class 'rpf'
plot(
  x,
  family = 1L,
  ...,
  type = c("tree", "boxes"),
  max_interaction = 2L,
  trees = NULL,
  class = NULL,
  factor_levels = TRUE
)

Arguments

x

[rpf]: A fitted rpf model.

family

[integer(1): 1]: Which tree family to extract, between 1 and n_families.

...

Reserved for future expansion.

type

[character(1): "tree"]: "tree" for a split diagram, "boxes" for the leaves in variable space.

max_interaction

[integer(1): 2]: Draw trees on up to this many variables, at most 2 for type = "boxes". The remaining trees are summarized.

trees

[character | NULL: NULL]: Names of the trees to draw, as in the tree column of rpf_boxes(), e.g. c("x1", "x1:x2"). NULL draws all trees up to max_interaction.

class

[character(1) | NULL: NULL]: For multiclass classification, the outcome level whose values to plot. NULL uses the first level.

factor_levels

[logical(1): TRUE]: Label factors with their levels instead of integer codes. Levels are in the order used in the fit: ordered by the outcome, or in their own order with keep_level_order (see rpf()).

Value

A ggplot for type = "tree", a patchwork for type = "boxes".

Details

• type = "tree" draws a split diagram in the style of Figure 1b of Hiabu et al. (2020), with the trees in rows by number of variables, each leaf filled by its value. The forest stores only its final leaves, not the order of splits or which leaf a tree grew from, so the splits within a tree are reconstructed: the diagram shows a split structure consistent with the leaves, and dashed lines link trees to the trees they could have grown from. Leaves that overlap other leaves of the same tree hang from dotted lines. Readable for small fits, e.g. n_families = 1 and n_splits = 10.

• type = "boxes" draws one panel per tree in variable space: leaves of trees on one variable as bars from zero to their value, leaves of trees on two variables as rectangles filled by their value. Overlapping leaves add up, which the translucent drawing shows.

Trees on more than max_interaction variables, and trees not in trees, are not drawn but summarized as a remainder, similar to the remainder term of predict_components().

After purify(), the trees are the components of the functional decomposition and only type = "boxes" applies. To plot components averaged over all tree families, use predict_components(), e.g. with the plotting functions in glex.

References

Hiabu, M., Mammen, E., Meyer, J. T. (2020). Random Planted Forest: a directly interpretable tree ensemble. arXiv:2012.14563.

Examples

rpfit <- rpf(mpg ~ wt + hp, data = mtcars, n_families = 1, n_splits = 10, prune = FALSE)
plot(rpfit)

purify(rpfit)
plot(rpfit, type = "boxes")