Provides a sampling of what’s available through sneer. But see the full Documentation for the available options.
res <- sneer(iris)res <- sneer(iris, indexes = 1:4, label_name = "Species", method = "pca")
# Same as above, but with sensible defaults (use all numeric columns, plot
# with first factor column found)
res <- sneer(iris, method = "pca")res <- sneer(iris, method = "pca", scale_type = "a")res <- sneer(iris, method = "pca", scale_type = "r")res <- sneer(iris, method = "pca", scale_type = "m")res <- sneer(iris, max_iter = 200)# full species name on plot is cluttered, so just use the first two
# letters and half size
res <- sneer(iris, scale_type = "a", label_chars = 2,
point_size = 0.5, plot_labels = TRUE)# Create a 5D gaussian with its own column specifying colors to use
# for each point (in this case, random)
g5d <- data.frame(matrix(rnorm(100 * 5), ncol = 5),
color = rgb(runif(100), runif(100), runif(100)),
stringsAsFactors = FALSE)
# Specify the name of the color column and the plot will use it rather than
# trying to map factor levels to colors
res <- sneer(g5d, method = "pca", color_name = "color")ggplot2 for the Embedding Plot# You need to install and load ggplot2 and RColorBrewer yourself
library("ggplot2")
res <- sneer(iris, method = "pca", scale_type = "a", plot_type = "g")# Use a different ColorBrewer palette, bigger points, and range scale each
# column
res <- sneer(iris, method = "pca", scale_type = "r", plot_type = "g",
color_scheme = "Dark2", label_size = 2)res <- sneer(iris, plot_type = "n")res <- sneer(iris, method = "mmds")# Sammon map starting from random distribution
res <- sneer(iris, method = "sammon", scale_type = "a", init = "r")# TSNE with a perplexity of 32, initialize from PCA
res <- sneer(iris, method = "tsne", scale_type = "a", init = "p",
perplexity = 32)
# default settings are to use TSNE with perplexity 32 and initialization
# from PCA so the following is the equivalent of the above
res <- sneer(iris, scale_type = "a")# Use the standard tSNE optimization method (Jacobs step size method) with
# step momentum. Range scale the matrix and use an aggressive learning
# rate (eta).
res <- sneer(iris, scale_type = "m", perplexity = 25, opt = "tsne",
eta = 500)res <- sneer(iris, scale_type = "m", perplexity = 25, opt = "tsne",
eta = 500, exaggerate = 4, exaggerate_off_iter = 100)# By default we initialize from a PCA scores plot, but we can initialize
# from random too
res <- sneer(iris, scale_type = "m", perplexity = 25, opt = "tsne",
eta = 500, exaggerate = 4, exaggerate_off_iter = 100,
init = "r")# Will step from a global-ish perplexity value towards a perplexity of 32
res <- sneer(iris, scale_type = "a", method = "nerv", perp_scale = "step")# NeRV method has a lambda parameter - closer to 1 it gets, the more it
# tries to avoid false positives (close points in the map that aren't close
# in the input space):
res <- sneer(iris, scale_type = "a", method = "nerv", perp_scale = "step",
lambda = 1)# Original NeRV paper transferred input exponential similarity kernel
# precisions to the output kernel, and initialized from a uniform random
# distribution
res <- sneer(iris, scale_type = "a", method = "nerv", perp_scale = "step",
lambda = 1, prec_scale = "t", init = "u")# Like NeRV, the JSE method also has a controllable parameter that goes
# between 0 and 1, called kappa. It gives similar results to NeRV at 0 and
# 1 but unfortunately the opposite way round! The following gives similar
# results to the NeRV embedding above:
res <- sneer(iris, scale_type = "a", method = "jse", perp_scale = "step",
kappa = 0)# Rather than step perplexities, use multiscaling to combine and average
# probabilities across multiple perplexities. Output kernel precisions
# can be scaled based on the perplexity value (compare to NeRV example
# which transferred the precision directly from the input kernel)
res <- sneer(iris, scale_type = "a", method = "jse", perp_scale = "multi",
prec_scale = "s")# HSSNE has a controllable parameter, alpha, that lets you control how
# much extra space to give points compared to the input distances.
# Setting it to 1 is equivalent to TSNE, so 1.1 is a bit of an extra push:
res <- sneer(iris, scale_type = "a", method = "hssne", alpha = 1.1)# Similar to HSSNE, but directly optimizes alpha along with the coordinates.
# Parameters associated with kernel optimization are passed via a "dyn" list.
# We wait 25 iterations here before starting to modify alpha from its initial
# value of 0, to allow the configuration to settle a little bit:
res <- sneer(iris, method = "dhssne", dyn = list(kernel_opt_iter = 25), alpha = 0)# Similar to DHSSNE, but directly optimizes a per-point degree of freedom
# Also recommended to wait a few iterations before starting optimization of
# each dof value from initial value of 10:
res <- sneer(iris, method = "itsne", dyn = list(kernel_opt_iter = 25), dof = 10)# ws-SNE treats the input probability like a graph where the probabilities
# are weighted edges and adds extra repulsion to nodes with higher degrees
res <- sneer(iris, scale_type = "a", method = "wssne")# can use a step-function input kernel to make input probability more like
# a k-nearest neighbor graph (but note that we don't take advantage of the
# sparsity for performance purposes, sadly)
res <- sneer(iris, scale_type = "a", method = "wtsne", perp_kernel_fun = "step")# export the distance matrices and do whatever quality measures we want at our
# leisure
res <- sneer(iris, scale_type = "a", method = "wtsne", ret = c("dx", "dy"))
# export degree centrality, input weight function precision parameters,
# and intrinsic dimensionality
res <- sneer(iris, scale_type = "a", method = "wtsne",
ret = c("deg", "prec", "dim"))If your dataset labels divide the data into natural classes, can calculate average area under the ROC and/or precision-recall curve too, but you need to have installed the PRROC package.
You can also calculate the Area Under the RNX Curve, which is a measure of neighborhood preservation between the upper and lower dimensions, which doesn’t need any categorization.
All these techniques can be slow (scale with the square of the number of observations).
# "n" - RNX AUC
res <- sneer(iris, scale_type = "a", method = "wtsne",
quality_measures = c("n"))
# Install and load the PRROC package
library(PRROC)
# "r" - ROC AUC
# "p" - Precision Recall AUC
res <- sneer(iris, quality_measures = c("n", "r", "p"))# export input (dx) and output (dy) distance matrices
res <- sneer(iris, scale_type = "a", method = "wtsne",
ret - c("dx", "dy"))
rnx_auc <- rnx_auc_embed(res$dx, res$dy)
pr_auc <- pr_auc_embed(res$dy, iris$Species)
roc_auc <- roc_auc_embed(res$dy, iris$Species)res <- sneer(iris)
# Plot the embedding as points colored by category, using the rainbow
# color ramp function:
embed_plot(res$coords, iris$Species, color_scheme = rainbow)
# You can pass in the entire data frame and sneer will try and find a suitable
# column
embed_plot(res$coords, iris, color_scheme = rainbow)res <- sneer(iris, scale_type = "a", method = "wtsne",
ret = c("deg", "prec", "dim"))
# Load the RColorBrewer Library
library(RColorBrewer)
# Use a ColorBrewer Qualitative palette using the name
# NB: string not a function!
embed_plot(res$coords, iris$Species, color_scheme = "Dark2")res <- sneer(iris, scale_type = "a", method = "wtsne",
ret = c("deg", "prec", "dim"))
# Load the RColorBrewer Library
library(RColorBrewer)
# Degree centrality
embed_plot(res$coords, res$deg)
# Intrinsic Dimensionality using the PRGn palette
# (requires RColorBrewer package to be installed)
embed_plot(res$coords, res$dim, color_scheme = "PRGn")
# Input weight function precision parameter with the Spectral palette
embed_plot(res$coords, res$prec, color_scheme = "Spectral")
# Calculate the 32-nearest neighbor preservation for each observation
# 0 means no neighbors preserved, 1 means all of them
pres32 <- nbr_pres(res$dx, res$dy, 32)
embed_plot(res$coords, pres32, cex = 1.5)An inhomogeneous t-SNE example:
# dof = 50, initial degrees of freedom for each point more Gaussian-like than
# t-distributed
# kernel_opt_iter = 0, don't wait any interations to start optimizing the d.o.f.
# ret = c("dyn"), export the optimized d.o.f.
iris_itsne <- sneer(iris, method = "itsne", kernel_opt_iter = 0, dof = 50,
ret = c("dyn"))
library(RColorBrewer)
# View the d.o.f on the output coordinates
# Dark blue means that point didn't need to stretch its output distances much
# compared to the input distances. Lighter colors indicate those that used
# a t-SNE-like stretching
embed_plot(iris_itsne$coords, iris_itsne$dyn$dof, color_scheme = "Blues")Using the embed_plotly function will open a browser window (unless you’re using RStudio, in which case it will appear in the Plots tab as usual).
# You need to install and load plotly yourself
library("plotly")
res <- sneer(iris, ret = c("deg", "prec", "dim"))
# Get a nice legend if you use categorical values to map colors
embed_plotly(res$coords, iris)
# Get a color scale if you use a numeric vector
embed_plotly(res$coords, res$dim)
# Can use different color schemes
embed_plotly(res$coords, iris, color_scheme = topo.colors)
# Can Use ColorBrewer names too
embed_plotly(res$coords, res$dim, color_scheme = "Blues")