Loom is a streaming inference and query engine for the Cross-Categorization model mansinghka2009cross, shafto2011probabilistic.
Loom learns models of sparse heterogeneous tabular data, with hundreds of features and millions of rows. Loom currently supports the following feature types and models:
See input format docs for details.
Loom targets tabular datasets of sizes 100-1000 columns 10^3-10^9 rows. To handle large datasets, loom implements subsample annealing obermeyer2014scaling with an accelerating annealing schedule and adaptively turns off ineffective inference strategies. Loom's annealing schedule is tuned to learn 10^8 cell datasets in under an hour and 10^10 cell datasets in under a day (depending on feature type and sparsity).
Full Inference: Partial Inference: Greedy Inference: structure hyperparameters hyperparameters mixtures mixtures mixtures |-------------------> ------------------> ------------------> 1 many-passes ~10^4 accelerate 10^9 single-pass 10^4 row rows rows row/sec
Loom is a streaming rewrite of the TARDIS engine developed by Eric Jonas https://twitter.com/stochastician at Prior Knowledge, Inc.
Loom relies heavily on the distributions library.
Copyright (c) 2014 Salesforce.com, Inc. All rights reserved. Copyright (c) 2015, Google, Inc.
Licensed under the Revised BSD License. See LICENSE.txt for details.
The PreQL query interface is covered by US patents pending: