t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
https://doi.org/10.1109/TVCG.2020.2986996
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89 lines
3.3 KiB
89 lines
3.3 KiB
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[](https://travis-ci.org/lvdmaaten/bhtsne)
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This software package contains a Barnes-Hut implementation of the t-SNE algorithm. The implementation is described in [this paper](http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf).
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**Note:** This repository is a version of t-SNE modified to support ongoing research. It may be slightly slower than the original. If you're just trying to run t-SNE, check the original repository that we forked from.
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# Installation #
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On Linux or OS X, compile the source using the following command:
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```
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g++ sptree.cpp tsne.cpp tsne_main.cpp -o bh_tsne -O2
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```
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The executable will be called `bh_tsne`.
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On Windows using Visual C++, do the following in your command line:
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- Find the `vcvars64.bat` file in your Visual C++ installation directory. This file may be named `vcvars64.bat` or something similar. For example:
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```
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// Visual Studio 12
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"C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\amd64\vcvars64.bat"
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// Visual Studio 2013 Express:
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C:\VisualStudioExp2013\VC\bin\x86_amd64\vcvarsx86_amd64.bat
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```
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- From `cmd.exe`, go to the directory containing that .bat file and run it.
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- Go to `bhtsne` directory and run:
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```
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nmake -f Makefile.win all
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```
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The executable will be called `windows\bh_tsne.exe`.
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# Usage #
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The code comes with wrappers for Matlab and Python. These wrappers write your data to a file called `data.dat`, run the `bh_tsne` binary, and read the result file `result.dat` that the binary produces. There are also external wrappers available for [Torch](https://github.com/clementfarabet/manifold), [R](https://github.com/jkrijthe/Rtsne), and [Julia](https://github.com/zhmz90/BHTsne.jl). Writing your own wrapper should be straightforward; please refer to one of the existing wrappers for the format of the data and result files.
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Demonstration of usage in Matlab:
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```matlab
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filename = websave('mnist_train.mat', 'https://github.com/awni/cs224n-pa4/blob/master/Simple_tSNE/mnist_train.mat?raw=true');
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load(filename);
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numDims = 2; pcaDims = 50; perplexity = 50; theta = .5; alg = 'svd';
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map = fast_tsne(digits', numDims, pcaDims, perplexity, theta, alg);
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gscatter(map(:,1), map(:,2), labels');
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```
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Demonstration of usage in Python:
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```python
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import numpy as np
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import bhtsne
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data = np.loadtxt("mnist2500_X.txt", skiprows=1)
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embedding_array = bhtsne.run_bh_tsne(data, initial_dims=data.shape[1])
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```
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### Python Wrapper
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Usage:
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```bash
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python bhtsne.py [-h] [-d NO_DIMS] [-p PERPLEXITY] [-t THETA]
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[-r RANDSEED] [-n INITIAL_DIMS] [-v] [-i INPUT]
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[-o OUTPUT] [--use_pca] [--no_pca] [-m MAX_ITER]
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```
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Below are the various options the wrapper program `bhtsne.py` expects:
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- `-h, --help` show this help message and exit
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- `-d NO_DIMS, --no_dims` NO_DIMS
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- `-p PERPLEXITY, --perplexity` PERPLEXITY
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- `-t THETA, --theta` THETA
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- `-r RANDSEED, --randseed` RANDSEED
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- `-n INITIAL_DIMS, --initial_dims` INITIAL_DIMS
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- `-v, --verbose`
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- `-i INPUT, --input` INPUT: the input file, expects a TSV with the first row as the header.
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- `-o OUTPUT, --output` OUTPUT: A TSV file having each row as the `d` dimensional embedding.
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- `--use_pca`
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- `--no_pca`
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- `-m MAX_ITER, --max_iter` MAX_ITER
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