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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,
metric = "euclidean",
learn_rate = 1,
epochs = NULL,
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")
)An updated version of recipe with the new step added to the
sequence of any existing operations.
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.
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.
A logical to indicate if the quantities for preprocessing have been estimated.
A call to vars to specify which variable is used as the
outcome in the encoding process (if any).
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.
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.
The effective minimum distance between embedded points.
Character, type of distance metric to use to find nearest
neighbors. See uwot::umap() for more details. Default to "euclidean".
Positive number of the learning rate for the optimization process.
Number of iterations for the neighbor optimization. See
uwot::umap() for more details.
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.
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()
or bake().
A character string for the prefix of the resulting new variables. See notes below.
A logical to keep the original variables in the
output. Defaults to FALSE.
Use keep_original_cols instead to specify whether the
original predictors should be retained along with the new embedding
variables.
An object that defines the encoding. This is NULL until the
step is trained by recipes::prep().
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.
A character string that is unique to this step to identify it.
When you tidy() this step, a tibble with columns terms
(the selectors or variables selected) is returned.
This step has 5 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)
The underlying operation does not allow for case weights.
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.
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.
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
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)
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