A pytorch implementation of Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry.
We are concerned with the discovery of hierarchical relationships from large-scale unstructured similarity scores. For this purpose, we study different models of hyperbolic space and find that learning embeddings in the Lorentz model is substantially more efficient than in the Poincaré-ball model. We show that the proposed approach allows us to learn high-quality embeddings of large taxonomies which yield improvements over Poincaré embeddings, especially in low dimensions. Lastly, we apply our model to discover hierarchies in two real-world datasets: we show that an embedding in hyperbolic space can reveal important aspects of a company’s organizational structure as well as reveal historical relationships between language families.
Binary tree embedding and visualization.
# See this for more options python lorentz.py --help python lorentz.py bin_mat # run binary tree # plot the checkpoint's embeddings for all saved checkpoints # in poincare space python lorentz.py bin_mat -plot -ckpt ckpt # plot only embeddings python lorentz.py bin_mat -plot -ckpt ckpt -plot_graph # plot graph also python lorentz.py bin_mat -plot -ckpt ckpt -plot_graph -overwrite_plots # overwrite plots python lorentz.py bin_mat -plot -ckpt ckpt -plot_graph -plot_size 10 # make a large plot
To embed an arbitrary graph
- Add a numpy matrix in the
datasets.pyfile with a unique name (
my_graphfor example). This represents a directed adjacency matrix
- Now you can simply call
python lorentz.py my_graphto embed your graph.
- You can use tensorboard to watch the progress with
tensorboard --logdir runs.
- You can plot the embeddings using
python lorentz.py my_graph -plot -ckpt ckpt
For anything else
python lorentz.py --help