cuda.ml (version 0.3.2)

cuda_ml_tsne: t-distributed Stochastic Neighbor Embedding.

Description

t-distributed Stochastic Neighbor Embedding (TSNE) for visualizing high- dimensional data.

Usage

cuda_ml_tsne(
  x,
  n_components = 2L,
  n_neighbors = ceiling(3 * perplexity),
  method = c("barnes_hut", "fft", "exact"),
  angle = 0.5,
  n_iter = 1000L,
  learning_rate = 200,
  learning_rate_method = c("adaptive", "none"),
  perplexity = 30,
  perplexity_max_iter = 100L,
  perplexity_tol = 1e-05,
  early_exaggeration = 12,
  late_exaggeration = 1,
  exaggeration_iter = 250L,
  min_grad_norm = 1e-07,
  pre_momentum = 0.5,
  post_momentum = 0.8,
  square_distances = TRUE,
  seed = NULL,
  cuML_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace")
)

Arguments

x

The input matrix or dataframe. Each data point should be a row and should consist of numeric values only.

n_components

Dimension of the embedded space.

n_neighbors

The number of datapoints to use in the attractive forces. Default: ceiling(3 * perplexity).

method

T-SNE method, must be one of "barnes_hut", "fft", "exact". The "exact" method will be more accurate but slower. Both "barnes_hut" and "fft" methods are fast approximations.

angle

Valid values are between 0.0 and 1.0, which trade off speed and accuracy, respectively. Generally, these values are set between 0.2 and 0.8. (Barnes-Hut only.)

n_iter

Maximum number of iterations for the optimization. Should be at least 250. Default: 1000L.

learning_rate

Learning rate of the t-SNE algorithm, usually between (10, 1000). If the learning rate is too high, then t-SNE result could look like a cloud / ball of points.

learning_rate_method

Must be one of "adaptive", "none". If "adaptive", then learning rate, early exaggeration, and perplexity are automatically tuned based on input size. Default: "adaptive".

perplexity

The target value of the conditional distribution's perplexity (see https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding for details).

perplexity_max_iter

The number of epochs the best Gaussian bands are found for. Default: 100L.

perplexity_tol

Stop optimizing the Gaussian bands when the conditional distribution's perplexity is within this desired tolerance compared to its taget value. Default: 1e-5.

early_exaggeration

Controls the space between clusters. Not critical to tune this. Default: 12.0.

late_exaggeration

Controls the space between clusters. It may be beneficial to increase this slightly to improve cluster separation. This will be applied after `exaggeration_iter` iterations (FFT only).

exaggeration_iter

Number of exaggeration iterations. Default: 250L.

min_grad_norm

If the gradient norm is below this threshold, the optimization will be stopped. Default: 1e-7.

pre_momentum

During the exaggeration iteration, more forcefully apply gradients. Default: 0.5.

post_momentum

During the late phases, less forcefully apply gradients. Default: 0.8.

square_distances

Whether TSNE should square the distance values.

seed

Seed to the psuedorandom number generator. Setting this can make repeated runs look more similar. Note, however, that this highly parallelized t-SNE implementation is not completely deterministic between runs, even with the same seed being used for each run. Default: NULL.

cuML_log_level

Log level within cuML library functions. Must be one of "off", "critical", "error", "warn", "info", "debug", "trace". Default: off.

Value

A matrix containing the embedding of the input data in a low- dimensional space, with each row representing an embedded data point.

Examples

# NOT RUN {
library(cuda.ml)

embedding <- cuda_ml_tsne(iris[1:4], method = "exact")

set.seed(0L)
print(kmeans(embedding, centers = 3))
# }