Project details for Loom

Logo Loom 0.2.10

by fritzo - March 19, 2015, 19:22:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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

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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