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.
When left at NA, a suitable value will be estimated automatically.
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
umap.defaults
custom.settings = umap.defaults
custom.settings$n_neighbors = 5
custom.settings