libFM with Early Stopping

Changes:

• Removed mcmc and sgda optimization methods
• Added validation data as mandatory parameter
• Implemented early stopping of fm model
• Validation data is MANDATORY for this verions
• Removed handling of Meta
• Removed regression task

Additional parameters:

• early stopping (bool) -- enabling early stopping
• num iterations for early stopping (int) -- how many iterations till break

Example

<path-to-libfm>/bin/libFM -task c -train <path-to-train-data> -test <path-to-test-data>
-validation <path-to-validation-data> -dim '1,1,8' -early_stop 1 -num_stop 15 
-out <where-predictions-to-save> -verbosity 0 -iter 40 
-method sgd -learn_rate 0.001 -init_stdev 0.0003

libFM

Library for factorization machines

web: http://www.libfm.org/

forum: https://groups.google.com/forum/#!forum/libfm

Factorization machines (FM) are a generic approach that allows to mimic most factorization models by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain. libFM is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least squares (ALS) optimization as well as Bayesian inference using Markov Chain Monte Carlo (MCMC).

Compile

libFM has been tested with the GNU compiler collection and GNU make. libFM and the tools can be compiled with

make all

Usage

Please see the libFM 1.4.2 manual for details about how to use libFM. If you have questions, please visit the forum.