tsne_grid: t-SNE grid search function
In Laurae2/Laurae: Advanced High Performance Data Science Toolbox for R

Description Usage Arguments Value Examples

This function allows you to search a perplexity hyperparameter range along with different seeds. Verbosity is automatic and cannot be removed. In case you need this function without verbosity, please compile the package after removing verbose messages.

tsne_grid(data, output_dims, input_dims = ncol(data),
  perplexity_range = c(1, min(floor((nrow(data) - 1)/3)), 1000), tries = 10,
  iterations = 10000, theta = 0, check_duplicates = FALSE, pca = FALSE,
  is_distance = FALSE)

`data`	The data.frame input into t-SNE
`output_dims`	How many dimensions to output? (increases exponentially the computation time)
`input_dims`	How many input dimensions to use? (defaults to `ncol(data)`) - this should be changed when using pca to a value below the default value
`perplexity_range`	What hyperparameter interval to look for? (should be formatted as (min, max)) - defaults to `c(1, min(floor((nrow(data)-1)/3)), 1000)` - to grid search a seed for a fixed perplexity value, use min = max as inputs - the best pragmatic perpelxity for the lowest loss is typically `floor((nrow(data)-1)/3)`. Make sure to avoid very high perplexity (like 1000) on large data (like 10000 observations). You might end up with a never ending tree creation, scaling quadratically (or even worse). By default, it is maxed to 1000.
`tries`	How many seeds to test t-SNE per perplexity value? (this increases linearly the computation time)
`iterations`	How many iterations per t-SNE are performed? (this increases approximately linearly the computation time)
`theta`	Use exact t-SNE (0) or Barnes-Hut t-SNE? (in ]0, 1] interval)
`check_duplicates`	Should t-SNE check for duplicates? (unlike common beliefs, t-SNE works perfectly with the existance of identical observations)
`pca`	Should a PCA (Principal Component Analysis) be performed? (note: it is performed every iteration, therefore it is computationally intensive and should be avoided - if you need PCA, please input the PCA instead of the data)
`is_distance`	Is the input a distance matrix? (assumes the diagonal cuts in half the input data.frame)

A list with the best (lowest loss at a specific iteration) t-SNE elements from Rtsne

#tsne_model <- tsne_grid(initial_diag = initial_diag, dims = 3,
#perplexity_range = c(floor((ncol(initial_diag)-1)/3), floor((ncol(initial_diag)-1)/3)),
#tries = 100, iterations = 10000, theta = 0.0, check_duplicates = FALSE,
#pca = FALSE, is_distance = TRUE)

Laurae2/Laurae documentation built on May 8, 2019, 7:59 p.m.