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ridge

Python Machine Learning Library specialized in L2R and Recommendation. Named after my favorite racing game R4.

日本語のREADMEはこちら.

Requirements

  • Python 3.6.0 ~
  • NumPy
  • Scipy

Implementation

These models are available.

Class Module Description Document
FMRegressor factorization_machines Factorization Machine for regression tasks FMs
FMClassifier factorization_machines Factorization Machine for classification tasks FMs
MatFac matrix_factorization Ordinal Matrix Factorization MFs
NNMatFac matrix_factorization Non-negative Matrix Factorization MFs
ConditionalLogit logit_models Conditional Logistic Regression as a discrete choice model LMs

Directories

name descripiton
ridge Model implementation
ridge.racer Cython optimization
docs Documents (usage of this package & memo related to implementation)
tests Model test suites

Models

MFs

MatFac (Matrix Factorization)

  • MovieLens 100k
    • with Cython 17m20s (1000epochs)
    • with Python 28m38s (1000epochs)

FMs

FMRegressor (Factorization Machine for Regression Tasks)

FMClassifier (Factorization Machine for Classification Tasks)

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