https://github.com/lenskit/eval-quickstart
Quick-start template for the LensKit evaluator.
https://github.com/lenskit/eval-quickstart
Last synced: 2 months ago
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Quick-start template for the LensKit evaluator.
- Host: GitHub
- URL: https://github.com/lenskit/eval-quickstart
- Owner: lenskit
- Created: 2015-04-25T22:28:45.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2018-04-28T15:12:29.000Z (over 7 years ago)
- Last Synced: 2024-04-16T02:03:26.024Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 186 KB
- Stars: 3
- Watchers: 6
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# LensKit Evaluation Quickstart
This example project shows how to run and evaluate a custom LensKit recommender component using
Gradle, the current recommended way to run LensKit evaluations. It is intended to serve as a
template for you to use when you create new LensKit evaluations.
The key user files that you are likely to want to edit are:
- `build.gradle`: to configure the build, add dependencies, etc.
- `eval.groovy`: to change the lenskit evaluation that is run,
perhaps by configuring different recommenders.
= `analyze-output.ipynb`: to change the analysis of the output data in `build`,
perhaps including the charts that are generated.
To run the evaluation, run:
./gradlew evaluate
As is typical with Gradle projects, all output files go in the `build` directory, where they can
be removed with `./gradlew clean`.
## Viewing Analysis
We have provided a [Jupyter](http://jupyter.org/) notebook, `analyze-output.ipynb`, as an example
of how to analyze the results of a LensKit evaluation in Python. To view this notebook, run
`jupyter notebook` (or `ipython notebook`) after you've run the evaluation; it will automatically
open a browser, and you can select the `analyze-output` notebook.
You can also run `./gradlew analyzeResults` to perform a batch run of the analysis; the output will
be in `build/analysis.html`.
The analysis requires the following software:
- Jupyter (formerly IPython) to view the notebook
- Pandas
- matplotlib
An easy way to get all of this software is to install the [Anaconda Python][ac] distribution. It
provides a complete scientific software stack for Python on Windows, OS X, and Linux.
The notebook is configured to run with the Python 2 kernel, but the code is entirely compatible with
Python 3.
[ac]: https://www.continuum.io/downloads
## Example Scorer
There is a simple example scorer in src/main/java. This scorer
includes a model that generates item mean ratings, and a scorer based on
that model. You may find the model and predictor useful as starting
points for your own predictors. The analysis script uses this
scorer along with some well-known rating prediction algorithms.
# More Information
More information on LensKit and its evaluator can be found on the [LensKit web site][web].
[web]: http://lenskit.org/documentation/
# Copyright
This project was created by the LensKit contributors, primarily Michael Ekstrand, Daniel Kluver,
and John Riedl.
The files in this project may be freely modified, used, and distributed without restriction.
If further legal clarity is required, these files are licensed under [Creative Commons CC0][CC0].
[CC0]: https://creativecommons.org/publicdomain/zero/1.0/