Embedding Neighborhoods Simultaneously by t-Distributed Stochastic Neighbor Embeding: ENS-t-SNE
Please see the following video for a demonstration and explanation (click on image):
An example program and visualization script can be found in enstsne.py
The Palmer's Penguins dataset captured in 3D by ENS-t-SNE.
The USDA Food Composition dataset as captured by ENS-t-SNE.
To import the ens-t-sne module, use
import mview
To run ens-t-sne and store results in an object mv
mv = mview.mpse_tsne(
data,
perplexity,
iters,
smart_init,
verbose,
show_plots,
save_results)
data is a list of either symmetric numpy distance matrices or nxd numpy high dimensional feature array.
perplexity is the perplexity parameter for the t-SNE optimizations, defaults to 30.
iters is the total number of updates to run in the optimization, defaults to 200.
smart_init is whether to use a smart initialization strategy (True) or to randomly initialize the optimization (False), defaults to True.
verbose is the verbosity level, defaults to 0.
show_plots is whether to plot results and call plt.show() after optimization, defaults to False
save_results is whether to store the various arrays produced by the algorithm into csv files. Defaults to False. If True, saves results to the mview/temp directory by default, but can be changed by passing a string output="path/to/directory" with a path to the desired destination.
For a full list of optional keyword arguments, see the documentation in the mview/mpse.py file.
The mview.mpse_tsne function returns an object with the following properties:
mpse_tsne.embedding is an nx3 array containing the 3-dimensional embedding of the dataset.
mpse_tsne.images is an mxnx2 array, the first index corresponding to the projection number, the second to each datapoint, and the third to it's x,y coordinates
mpse_tsne.projections is an mx3x2 array, with the first index corresponding to the projection number, and the remaining two being the 3x2 projection matrix.