A package for training, evaluation, and visualization of the Latent Skill Embedding model. Read more about the model at https://siddharth.io/lentil.
You can install the package's dependencies with
pip install -r requirements.txt
You can install the package in your environment with
python setup.py install
If you wish to run the tests, make sure you have tox installed and then run
tox
Once installed in your environment, command-line interfaces for training and
evaluation are available through lse_train and lse_eval. The appropriate format for input
interaction log data is given in the documentation for lentil.datatools.InteractionHistory.
IPython notebooks used to conduct experiments are available in the nb directory, and provide
example invocations of most functions and classes. It is recommended that you read the notebooks
in the following order: toy_examples, synthetic_experiments, data_explorations,
model_explorations, evaluations, sensitivity_analyses, and bubble_experiments.
To create the transition graph visualizations in nb/data_explorations.ipynb, you will need to install pygraphviz.
Build the documentation with
tox -e docs
Once run, open doc/_build/html/index.html for Sphinx documentation on modules in the package.
Please contact the author at sgr45 [at] cornell [dot] edu if you have questions or find bugs.
If you find this software useful in your work, we kindly request that you cite the following paper:
@InProceedings{Reddy/etal/16c,
title={Latent Skill Embedding for Personalized Lesson Sequence Recommendation},
author={Reddy, Siddharth and Labutov, Igor and Joachims, Thorsten},
booktitle={Arxiv 1602.07029},
year={2016},
url={http://arxiv.org/abs/1602.07029}
}