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)