step_umap  R Documentation 
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")
)
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 
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 
neighbors 
An integer for the number of nearest neighbors used to
construct the target simplicial set. If 
num_comp 
An integer for the number of UMAP components. If 
min_dist 
The effective minimum distance between embedded points. 
metric 
Character, type of distance metric to use to find nearest
neighbors. See 
learn_rate 
Positive number of the learning rate for the optimization process. 
epochs 
Number of iterations for the neighbor optimization. See

options 
A list of options to pass to 
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 
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 
retain 
Use 
object 
An object that defines the encoding. This is 
skip 
A logical. Should the step be skipped when the
recipe is baked by 
id 
A character string that is unique to this step to identify it. 
UMAP, short for Uniform Manifold Approximation and Projection, is a nonlinear dimension reduction technique that finds local, lowdimensional 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
.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
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.
McInnes, L., & Healy, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. https://arxiv.org/abs/1802.03426.
"How UMAP Works" https://umaplearn.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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