uwot (version 0.0.0.9004)

umap_transform: Add New Points to an Existing Embedding

Description

Carry out an embedding of new data using an existing embedding. Requires using the result of calling umap or tumap with ret_model = TRUE.

Usage

umap_transform(X, model, init_weighted = TRUE, search_k = NULL,
  n_epochs = NULL, n_threads = max(1,
  RcppParallel::defaultNumThreads()/2), grain_size = 1,
  verbose = FALSE)

Arguments

X

The new data to be transformed, either a matrix of data frame. Must have the same columns in the same order as the input data used to generate the model.

model

Data associated with an existing embedding.

init_weighted

If TRUE, then initialize the embedded coordinates of X using a weighted average of the coordinates of the nearest neighbors from the original embedding in model, where the weights used are the edge weights from the UMAP smoothed knn distances. Otherwise, use an unweighted average.

search_k

Number of nodes to search during the neighbor retrieval. The larger k, the more the accurate results, but the longer the search takes. Default is the value used in building the model is used.

n_epochs

Number of epochs to use during the optimization of the embedded coordinates. A value between 30 - 100 is a reasonable trade off between speed and thoroughness. By default, this value is set to one third the number of epochs used to build the model.

n_threads

Number of threads to use. Default is half that recommended by RcppParallel.

grain_size

Minimum batch size for multithreading. If the number of items to process in a thread falls below this number, then no threads will be used. Used in conjunction with n_threads.

verbose

If TRUE, log details to the console.

Value

A matrix of coordinates for X transformed into the space of the model.

Examples

# NOT RUN {
iris_train <- iris[1:100, ]
iris_test <- iris[101:150, ]

# You must set ret_model = TRUE to return extra data needed
iris_train_umap <- umap(iris_train, verbose = TRUE, ret_model = TRUE)
iris_test_umap <- umap_transform(iris_test, iris_train_umap, verbose = TRUE)
# }