Description:

Loom is a streaming inference and query engine for the Cross-Categorization model.

Data Types

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:

• boolean fields as Beta-Bernoulli
• categorical fields with up to 256 values as Dirichlet-Discrete
• unbounded categorical fields as Dirichlet-Process-Discrete
• count fields as Gamma-Poisson
• real fields as Normal-Inverse-Chi-Squared-Normal
• sparse real fields as mixture of degenerate and dense real
• text and keyword fields as booleans for word absence/presence
• date fields as a combination of absolute, relative, and cyclic parts
• optional fields as a boolean plus one of the above feature models

Data Scale

Loom targets tabular datasets of sizes 100-1000 columns x 10^3-10^9 rows. To handle large datasets, loom implements subsample annealing 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).

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Ubuntu

Data Formats: Csv

Tags: Mcmc, Bayesian, Nonparametric

Archive: download here

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