R: Default configuration for umap

umap.defaults {umap} | R Documentation

## Default configuration for umap

### Description

A list with parameters customizing a UMAP embedding. Each component of the list is an effective argument for umap().

### Usage

    umap.defaults

### Format

An object of class `umap.config` of length 22.

### Details

n_neighbors: integer; number of nearest neighbors

n_components: integer; dimension of target (output) space

metric: character or function; determines how distances between data points are computed. When using a string, available metrics are: euclidean, manhattan. Other available generalized metrics are: cosine, pearson, pearson2. Note the triangle inequality may not be satisfied by some generalized metrics, hence knn search may not be optimal. When using metric.function as a function, the signature must be function(matrix, origin, target) and should compute a distance between the origin column and the target columns

n_epochs: integer; number of iterations performed during layout optimization

input: character, use either "data" or "dist"; determines whether the primary input argument to umap() is treated as a data matrix or as a distance matrix

init: character or matrix. The default string "spectral" computes an initial embedding using eigenvectors of the connectivity graph matrix. An alternative is the string "random", which creates an initial layout based on random coordinates. This setting.can also be set to a matrix, in which case layout optimization begins from the provided coordinates.

min_dist: numeric; determines how close points appear in the final layout

set_op_ratio_mix_ratio: numeric in range [0,1]; determines who the knn-graph is used to create a fuzzy simplicial graph

local_connectivity: numeric; used during construction of fuzzy simplicial set

bandwidth: numeric; used during construction of fuzzy simplicial set

alpha: numeric; initial value of "learning rate" of layout optimization

gamma: numeric; determines, together with alpha, the learning rate of layout optimization

negative_sample_rate: integer; determines how many non-neighbor points are used per point and per iteration during layout optimization

a: numeric; contributes to gradient calculations during layout optimization. When left at NA, a suitable value will be estimated automatically.

b: numeric; contributes to gradient calculations during layout optimization.

spread: numeric; used during automatic estimation of a/b parameters.

random_state: integer; seed for random number generation used during umap()

transform_state: integer; seed for random number generation used during predict()

knn: object of class umap.knn; precomputed nearest neighbors

knn.repeat: number of times to restart knn search

verbose: logical or integer; determines whether to show progress messages

umap_learn_args: vector of arguments to python package umap-learn

### Examples

    # display all default settings
    umap.defaults

    # create a new settings object with n_neighbors set to 5
    custom.settings = umap.defaults
    custom.settings$n_neighbors = 5
    custom.settings

[Package umap version 0.2.7.0 Index]