t-SNE Visualization
This repository is an easy-to-run t-SNE visualization tool for your dataset of choice. It currently supports 2D and 3D plots as well as an optional original image overlay on top of the 2D points.
Installation
Ubuntu Installation
First clone this repository, then install the TkInter package by running:
sudo apt-get install python3-tk
Optionally create a virtualenv for this project:
cd tsne-vis
virtualenv -p python3
source venv/bin/activate
Then install the python3 dependecies:
cd tsne-vis
pip install -r requirements.txt
Usage
Example Command
python main.py --num_samples=5000 --num_dimensions=2 --compute_embeddings=False --with_images=False
This will plot a 2D t-SNE plot with no image overlay. Note that the example code uses the Fashion-MNIST dataset which you can download by running:
chmod +x download_data.sh
./download_data.sh
You'll only need to modify the load_data method if you're planning on using your own dataset. Make sure it returns a set of numpy arrays: for example, if embedding grasycale images, you'll want to return an array of images and their associated labels as follows
X: (100, 32, 32)
y: (100,)
To see all possible command line options, run
python main.py --help
which will print:
usage: main.py [-h] [--num_samples NUM_SAMPLES]
[--num_dimensions NUM_DIMENSIONS] [--shuffle SHUFFLE]
[--compute_embeddings COMPUTE_EMBEDDINGS]
[--with_images WITH_IMAGES] [--random_seed RANDOM_SEED]
[--data_dir DATA_DIR] [--plot_dir PLOT_DIR]
t-SNE Visualizer
optional arguments:
-h, --help show this help message and exit
Setup:
--num_samples NUM_SAMPLES
# of samples to compute embeddings on. Becomes slow if
very high.
--num_dimensions NUM_DIMENSIONS
# of tsne dimensions. Can be 2 or 3.
--shuffle SHUFFLE Whether to shuffle the data before embedding.
--compute_embeddings COMPUTE_EMBEDDINGS
Whether to compute embeddings. Do this once per sample
size.
--with_images WITH_IMAGES
Whether to overlay images on data points. Only works
with 2D plots.
--random_seed RANDOM_SEED
Seed to ensure reproducibility
Path Params:
--data_dir DATA_DIR Directory where data is stored
--plot_dir PLOT_DIR Directory where plots are saved
Image Overlay
The overlay option only works for 2D plots and relies on matplotlib's AnnotationBox method. Here's an example of what it outputs:
