The librec-auto project aims to automate recommender system experimens using LibRec.
librec-auto is a Python tool for running recommender systems experiments.
It is built on top of the open-source LibRec_ package, and
can take advantage of the many algorithm and metric implementations there.
.. _LibRec: https://github.com/guoguibing/librec
The basic element of
librec-auto execution is the "study", which is a series
of experiments carried with a single algorithm, a single data set, and a set
of evaluation metrics. The experiments differ from each other by the hyperparameters
given to the algorithm.
librec-auto allows such studies to be conducted with
minimal experimenter intervention, and supports such capabilities as:
- recommendation result re-ranking
- re-running evaluations without re-computing results
- summarizing results in output graphs
- integration with Slack and Dropbox
The workflow of an study involves identifying appropriate data, creating training / test splits, implementing or choosing algorithms, running experiments (possibly with a range of different parameters), and reporting on the results.
Librec-auto uses an XML-based configuration system similar to Maven or Ant.
This directory contains the Python libraries for the librec_auto module. There are two other affiliated respositories:
librec-auto-java_: Contains the java source for the wrapper between LibRec and librec-auto, which is implemented in the
librec-auto-sample_: Contains sample data and configuration files that can be used to explore the functionality of librec-auto
.. _librec-auto-java: https://github.com/that-recsys-lab/librec-auto-java .. _librec-auto-sample: https://github.com/that-recsys-lab/librec-auto-sample
/jar: Contains the jar files for LibRec and the wrapper.
/rules: Contains the rules for translating configuration data to LibRec properties format.
/librec_auto: Contains the Python code for the project.
/doc: Contains documentation for the project
/test: Contains the unit tests (not many right now)
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