tf2-fm-zoo 0.1.1

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tf2-fm-zoo

Python package for the factorization machine implementations from tensorflow2_model_zoo.

Acknowledgement

The original implementation for the methods in this repo were done by Ren Zhang who kindly granted permission to use his code for the creation of the package.

Installation

pip install tf2_fm_zoo

Basic Example

import tensorflow as tf
import numpy as np
import pandas as pd

from sklearn.preprocessing import KBinsDiscretizer
from sklearn.datasets import load_boston

from fm_zoo.fm import FactorizationMachine


X, y = load_boston(return_X_y=True)

X = X[:,:3]
y = tf.cast(y, dtype=tf.float32)

kbd = KBinsDiscretizer(n_bins=15, encode="ordinal")

nunique_vals = pd.DataFrame(X).nunique()
X = tf.cast(kbd.fit_transform(X), dtype=tf.int64)

fm = FactorizationMachine(
    feature_cards=tf.cast(nunique_vals, tf.int32), 
    factor_dim=3)

fm.compile(loss=tf.keras.losses.mean_squared_error, optimizer="Adam")
hist = fm.fit(
    X, y, 
    validation_split=0.15, 
    batch_size=16,
    epochs=100,
    callbacks=[
      tf.keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True)
    ])

pd.DataFrame(hist.history).plot(figsize=(15,10))

Supported Models

Model	Reference	Year
FM	Factorization Machines	2010
FFM	Field-aware factorization machines for CTR prediction	2016
FNN	Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction	2016
AFM	Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks	2017
DeepFM	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction	2017
NFM	Nerual Factorization Machines for Sparse Predictive Analytics	2017
xDeepFM	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems	2018
AutoInt	AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks	2018
FNFM	Field-aware Neural Factorization Machine for Click-Through Rate Prediction	2019