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.

Usage

step_umap(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  outcome = NULL,
  neighbors = 15,
  num_comp = 2,
  min_dist = 0.01,
  metric = "euclidean",
  learn_rate = 1,
  epochs = NULL,
  initial = "spectral",
  target_weight = 0.5,
  options = list(verbose = FALSE, n_threads = 1),
  seed = sample(10^5, 2),
  prefix = "UMAP",
  keep_original_cols = FALSE,
  retain = deprecated(),
  object = NULL,
  skip = FALSE,
  id = rand_id("umap")
)

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 this step. See selections() for more details.
role: For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from 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. If neighbors is greater than the number of data points, the smaller value is used.
num_comp: An integer for the number of UMAP components. If num_comp is greater than the number of selected columns minus one, the smaller value is used.
min_dist: The effective minimum distance between embedded points.
metric: Character, type of distance metric to use to find nearest neighbors. See uwot::umap() for more details. Default to "euclidean".
learn_rate: Positive number of the learning rate for the optimization process.
epochs: Number of iterations for the neighbor optimization. See uwot::umap() for more details.
initial: Character, Type of initialization for the coordinates. Can be one of "spectral", "normlaplacian", "random", "lvrandom", "laplacian", "pca", "spca", "agspectral", or a matrix of initial coordinates. See uwot::umap() for more details. Default to "spectral".
target_weight: Weighting factor between data topology and target topology. A value of 0.0 weights entirely on data, a value of 1.0 weights entirely on target. The default of 0.5 balances the weighting equally between data and target.
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 recipes::prep or recipes::bake.
prefix: A character string for the prefix of the resulting new variables. See notes below.
keep_original_cols: A logical to keep the original variables in the output. Defaults to FALSE.
retain: Use keep_original_cols instead to specify whether the original predictors should be retained along with the new embedding variables.
object: An object that defines the encoding. This is NULL until the step is trained by recipes::prep().
skip: A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() 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.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

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).

The argument num_comp controls the number of components that will be retained (the original variables that are used to derive the components are removed from the data). The new components will have names that begin with prefix and a sequence of numbers. The variable names are padded with zeros. For example, if num_comp < 10, their names will be UMAP1 - UMAP9. If num_comp = 101, the names would be UMAP1 - UMAP101.

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms: character, the selectors or variables selected
id: character, id of this step

Tuning Parameters

This step has 7 tuning parameters:

num_comp: # Components (type: integer, default: 2)
neighbors: # Nearest Neighbors (type: integer, default: 15)
min_dist: Min Distance between Points (type: double, default: 0.01)
learn_rate: Learning Rate (type: double, default: 1)
epochs: # Epochs (type: integer, default: NULL)
initial: UMAP Initialization (type: character, default: spectral)
target_weight: Proportion Supervised (type: double, default: 0.5)

Case weights

The underlying operation does not allow for case weights.

Saving prepped recipe object

This recipe step may require native serialization when saving for use in another R session. To learn more about serialization for prepped recipes, see the bundle package.

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

if (FALSE) { # rlang::is_installed("ggplot2") && rlang::is_installed("irlba", version = "2.3.5.2")
library(recipes)
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 = UMAP1, y = UMAP2, col = Species)) +
  geom_point(alpha = .5)
}