Supervised and unsupervised uniform manifold approximation and projection (UMAP)

step_umap creates a specification of a recipe step that will project a set of features into a smaller space.

step_umap(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  outcome = NULL,
  neighbors = 15,
  num_comp = 2,
  min_dist = 0.01,
  learn_rate = 1,
  epochs = NULL,
  options = list(verbose = FALSE, n_threads = 1),
  seed = sample(10^5, 2),
  retain = FALSE,
  object = NULL,
  skip = FALSE,
  id = rand_id("umap")
)

# S3 method for step_umap
tidy(x, ...)

Arguments

recipe	A recipe object. The step will be added to the sequence of operations for this recipe.
...	One or more selector functions to choose variables. For step_umap, this indicates the variables to be encoded into a numeric format. Numeric and factor variables can be used. See recipes::selections() for more details. For the tidy method, these are not currently used.
role	For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new embedding columns created by the original variables will be used as predictors in a model.
trained	A logical to indicate if the quantities for preprocessing have been estimated.
outcome	A call to vars to specify which variable is used as the outcome in the encoding process (if any).
neighbors	An integer for the number of nearest neighbors used to construct the target simplicial set.
num_comp	An integer for the number of UMAP components.
min_dist	The effective minimum distance between embedded points.
learn_rate	Positive number of the learning rate for the optimization process.
epochs	Number of iterations for the neighbor optimization. See uwot::umap() for mroe details.
options	A list of options to pass to uwot::umap(). The arguments X, n_neighbors, n_components, min_dist, n_epochs, ret_model, and learning_rate should not be passed here. By default, verbose and n_threads are set.
seed	Two integers to control the random numbers used by the numerical methods. The default pulls from the main session's stream of numbers and will give reproducible results if the seed is set prior to calling prep.recipe() or bake.recipe().
retain	A single logical for whether the original predictors should be kept (in addition to the new embedding variables).
object	An object that defines the encoding. This is NULL until the step is trained by recipes::prep.recipe().
skip	A logical. Should the step be skipped when the recipe is baked by recipes::bake.recipe()? While all operations are baked when recipes::prep.recipe() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations
id	A character string that is unique to this step to identify it.
x	A step_umap object.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with a column called terms (the selectors or variables for embedding) is returned.

Details

UMAP, short for Uniform Manifold Approximation and Projection, is a nonlinear dimension reduction technique that finds local, low-dimensional representations of the data. It can be run unsupervised or supervised with different types of outcome data (e.g. numeric, factor, etc).

References

McInnes, L., & Healy, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. https://arxiv.org/abs/1802.03426.

"How UMAP Works" https://umap-learn.readthedocs.io/en/latest/how_umap_works.html

Examples

library(recipes)
library(dplyr)
library(ggplot2)

split <- seq.int(1, 150, by = 9)
tr <- iris[-split, ]
te <- iris[ split, ]

set.seed(11)
supervised <- 
  recipe(Species ~ ., data = tr) %>%
  step_center(all_predictors()) %>% 
  step_scale(all_predictors()) %>% 
  step_umap(all_predictors(), outcome = vars(Species), num_comp = 2) %>% 
  prep(training = tr)

theme_set(theme_bw())

bake(supervised, new_data = te, Species, starts_with("umap")) %>% 
  ggplot(aes(x = umap_1, y = umap_2, col = Species)) + 
  geom_point(alpha = .5)