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Polynomial regression and classification with sklearn and tensorflow

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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)
```

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