textSNE

Python code for rendering t-SNE code with text labels for each point.

See test-output.expected.png for an example of the sort of visualization this code will perform.

t-SNE is: van der Maaten, L. J. P. and Hinton, G. E. (2008) Visualizing Data using t-SNE. Journal of Machine Learning Research, Vol 9, (Nov) pp 2579-2605.

Where noted in header code or by directory name, I have included 3rd-party code.

My main change from the original t-SNE implementation is that I disable PCA as a preprocessing step, unless specifically explicitly by a function parameter. Since my data is high-dimensional and sparse, PCA is painfully slow.

To get started:

1. Unpack the original tSNE package: cd 3rd-party/t-SNE_files/ tar zxvf tSNE_linux.tar.gz If you are on a different architecture, you will have to unzip another package.

Alternately, you can use the pure Python implementation of t-SNE by replacing all code that reads: from calc_tsne import tsne with the following code: from tsne import tsne You will need matplotlib to run the pure Python implementation. However,

2. (Optional) Edit render.py and change DEFAULT_FONT to a TTF file containing a font you like.

3. Run ./test.py to test your installation. This will generate file 'test-output.rendered.png'. Note that 'test-output.rendered.png' and 'test-output.expected.png' are different, because each invockation of tSNE_linux uses a different random initialization.

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REQUIREMENTS: imagemagick: We use convert at the end of render.render, to flatten an image. Type: which convert as a test to see if you have this executable. You could perhaps remove this image flattening step, if you like.