cuml_umap: Uniform Manifold Approximation and Projection (UMAP) for...
In cuml: R Interface for the RAPIDS cuML Suite of Libraries

Description Usage Arguments Value Examples

Run the Uniform Manifold Approximation and Projection (UMAP) algorithm to find a low dimensional embedding of the input data that approximates an underlying manifold.

cuml_umap(
  x,
  y = NULL,
  n_components = 2L,
  n_neighbors = 15L,
  n_epochs = 500L,
  learning_rate = 1,
  init = c("spectral", "random"),
  min_dist = 0.1,
  spread = 1,
  set_op_mix_ratio = 1,
  local_connectivity = 1L,
  repulsion_strength = 1,
  negative_sample_rate = 5L,
  transform_queue_size = 4,
  a = NULL,
  b = NULL,
  target_n_neighbors = n_neighbors,
  target_metric = c("categorical", "euclidean"),
  target_weight = 0.5,
  transform_input = TRUE,
  seed = NULL,
  cuml_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace")
)

`x`	The input matrix or dataframe. Each data point should be a row and should consist of numeric values only.
`y`	An optional numeric vector of target values for supervised dimension reduction. Default: NULL.
`n_components`	The dimension of the space to embed into. Default: 2.
`n_neighbors`	The size of local neighborhood (in terms of number of neighboring sample points) used for manifold approximation. Default: 15.
`n_epochs`	The number of training epochs to be used in optimizing the low dimensional embedding. Default: 500.
`learning_rate`	The initial learning rate for the embedding optimization. Default: 1.0.
`init`	Initialization mode of the low dimensional embedding. Must be one of "spectral", "random". Default: "spectral".
`min_dist`	The effective minimum distance between embedded points. Default: 0.1.
`spread`	The effective scale of embedded points. In combination with `min_dist` this determines how clustered/clumped the embedded points are. Default: 1.0.
`set_op_mix_ratio`	Interpolate between (fuzzy) union and intersection as the set operation used to combine local fuzzy simplicial sets to obtain a global fuzzy simplicial sets. Both fuzzy set operations use the product t-norm. The value of this parameter should be between 0.0 and 1.0; a value of 1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy intersection. Default: 1.0.
`local_connectivity`	The local connectivity required – i.e. the number of nearest neighbors that should be assumed to be connected at a local level. Default: 1.
`repulsion_strength`	Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples. Default: 1.0.
`negative_sample_rate`	The number of negative samples to select per positive sample in the optimization process. Default: 5.
`transform_queue_size`	For transform operations (embedding new points using a trained model this will control how aggressively to search for nearest neighbors. Default: 4.0.
`a, b`	More specific parameters controlling the embedding. If not set, then these values are set automatically as determined by `min_dist` and `spread`. Default: NULL.
`target_n_neighbors`	The number of nearest neighbors to use to construct the target simplcial set. Default: n_neighbors.
`target_metric`	The metric for measuring distance between the actual and and the target values (`y`) if using supervised dimension reduction. Must be one of "categorical", "euclidean". Default: "categorical".
`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.
`transform_input`	If TRUE, then compute an approximate representation of the input data. Default: TRUE.
`seed`	Optional seed for pseudo random number generator. Default: NULL. Setting a PRNG seed will enable consistency of trained embeddings, allowing for reproducible results to 3 digits of precision, but at the expense of potentially slower training and increased memory usage. If the PRNG seed is not set, then the trained embeddings will not be deterministic.
`cuml_log_level`	Log level within cuML library functions. Must be one of "off", "critical", "error", "warn", "info", "debug", "trace". Default: off.

A UMAP model object that can be used as input to the cuml_transform() function. If transform_input is set to TRUE, then the model object will contain a "transformed_data" attribute containing the lower dimensional embedding of the input data.

library(cuml)

model <- cuml_umap(
  x = iris[1:4],
  y = iris[[5]],
  n_components = 2,
  n_epochs = 200,
  transform_input = TRUE
)

set.seed(0L)
print(kmeans(model$transformed, iter.max = 100, centers = 3))