# Supervised and unsupervised uniform manifold approximation and projection (UMAP)

Source:`R/umap.R`

`step_umap.Rd`

`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

`prep()`

or`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 retruned with
columns `terms`

and `id`

:

- terms
character, the selectors or variables selected

- id
character, id of this step

## Tuning Parameters

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)

## 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)
}
```