README.md

wgcca

Python Implementation of Weighted Generalized Canonical Correlation Analysis as described in "Learning Multiview Embeddings of Twitter Users". Benton A, Arora R, and Dredze M. ACL 2016.

Tested with

• Python 2.7
• scipy 0.17.0
• numpy 1.10.4

Test suite:

python src/wgccaTest.py

Sample call to learn 5-dimensional WGCCA model (first two views weighted twice as much as second two):

python src/wgcca.py --input resources/sample_wgcca_input.tsv.gz --output wgcca_embeddings.npz --model wgcca_model.pickle --k 5 --kept_views 0 1 2 3 --weights 1.0 1.0 0.5 0.5 --reg 1.e-8 1.e-8 1.e-8 1.e-8    

• Input format can be grokked from: resources/sample_wgcca_input.tsv
• WGCCA model saved to: wgcca_model.pickle
• WGCCA embeddings saved to: wgcca_embeddings.npz

WeightedGCCA methods

• _compute: look at this if you want to know how embeddings are computed
• learn: entrypoint for learning WeightedGCCA model from training set
• apply: entrypoint for extracting embeddings from new data

The input views used in "Learning Multiview Embeddings of Twitter Users" can be found at http://www.cs.jhu.edu/~mdredze/datasets/multiview_embeddings/ -- in the same format as resources/sample_wgcca_input.tsv.

If you use this code please cite:

Adrian Benton, Raman Arora, and Mark Dredze. Learning Multiview Representations of Twitter Users. Association for Computational Linguistics (ACL), 2016.

Please contact adrian dot author1_surname at gmail dot com if you have any questions/suggestions/concerns/comments.