LensKit

LensKit is a set of Python tools for experimenting with and studying recommender systems. It provides support for training, running, and evaluating recommender algorithms in a flexible fashion suitable for research and education.

LensKit for Python (also known as LKPY) is the successor to the Java-based LensKit project.

Installation

To install the current release with Anaconda (recommended):

conda install -c lenskit lenskit

Or you can use pip:

pip install lenskit

To use the latest development version, install directly from GitHub:

pip install git+https://github.com/lenskit/lkpy

Then see Getting Started.

Resources

• Mailing list, etc.

• Source and issues on GitHub

Contents:

• Getting Started
  • Setup
  • Loading Data
  • Defining Algorithms
  • Running the Evaluation
• Algorithm Interfaces
  • Base Algorithm
  • Recommendation
  • Candidate Selection
  • Rating Prediction
• Crossfold preparation
  • Row-based splitting
  • User-based splitting
  • Utility Classes
• Batch-Running Recommenders
  • Recommendation
  • Rating Prediction
  • Scripting Evaluation
• Evaluating Recommender Output
  • Prediction Accuracy Metrics
  • Top-N Evaluation
  • Loading Outputs
• Errors and Diagnostics
  • Logging
  • Warnings
• Algorithms
  • Basic and Utility Algorithms
  • k-NN Collaborative Filtering
  • Classic Matrix Factorization
  • Hierarchical Poisson Factorization
  • Implicit
• Utility Functions
  • Matrix Utilities
  • Math utilities
  • Miscellaneous
• Release Notes
  • 0.6.1
  • 0.6.0
  • 0.5.0
  • 0.3.0
  • 0.2.0
  • 0.1.0

Indices and tables

• Index

• Module Index

• Search Page