https://github.com/gmodena/tensor-fm

Polynomial regression and classification with sklearn and tensorflow
https://github.com/gmodena/tensor-fm

machine-learning sklearn-compatible sklearn-estimator tensorflow2

Last synced: about 1 year ago
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Polynomial regression and classification with sklearn and tensorflow

• Host: GitHub
• URL: https://github.com/gmodena/tensor-fm
• Owner: gmodena
• License: mit
• Created: 2019-10-24T22:01:58.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-11-09T08:49:17.000Z (over 2 years ago)
• Last Synced: 2025-04-07T21:12:12.738Z (over 1 year ago)
• Topics: machine-learning, sklearn-compatible, sklearn-estimator, tensorflow2
• Language: Python
• Homepage: http://nowave.it/factorization-machines-with-tensorflow.html
• Size: 42 KB
• Stars: 11
• Watchers: 2
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
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# tensor-fm

A scikit-learn compatible order 2 Factorization Machine, implemented atop TensorFlow 2.

The algorithm is described in http://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf. For an higher level

overview of the method see http://nowave.it/factorization-machines-with-tensorflow.html.

This package is a port to Tensorflow 2 of the code presented in that blog post. The goal of this project is

to experiment with different optimization strategies for classical ML models, and scalability of

TF2 backends.

## Install

The latest development version of `tensorfm` can be installed from its

[github repo](git+https://github.com/gmodena/tensor-f) with:

```

pip install git+https://github.com/gmodena/tensor-fm

```

## Usage examples

Tensorlow and scikit-learn APIs are provided.

### Tensorflow 

The tensorflow implementation of Factorization Machines lives under `tensor-fm/tensorfm/base.py`.

An example of how to work with this API can be found in `tensor-fm/tests/test_base.py`. 

### Scikit-learn estimator

`tensorfm.sklearn` exposes two sklearn compatible estimators: `FactorizationMachineRegressor`

and `FactorizationMachineClassifier`.

Example

```

from tensorfm.sklearn import FactorizationMachineRegressor

...

fm = FactorizationMachineRegressor()

fm.fit(X, y)

fm.predict(X)

```

See also `examples/movielens.py`

## Performance

All parameters and settings being equal, I noticed a considerable performance degradation of

`FactorizationMachineRegressor` (MSE on train/test) on movielens compared to the tensorflow 1 implementation

from http://nowave.it/factorization-machines-with-tensorflow.html.

Possibly related, a test in the `check_regressors_train` suite (`sklearn`) fails due to a low `R^2`. As a workaround

`FactorizationMachineRegressor` sets the `poor_score` tag to `True`.

## Limitations and known issues

Operations on sparse matrices are currently not supported.

Training continues till `max_iter` is reached, we should stop if performance does not improve for a certain number

of iterations.