# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen, weichenswc@163.com
Reference:
[1] Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617, 2017.
(https://arxiv.org/abs/1708.04617)
"""
from tensorflow.python.keras.models import Model
from ..feature_column import build_input_features, get_linear_logit, DEFAULT_GROUP_NAME, input_from_feature_columns
from ..layers.core import PredictionLayer
from ..layers.interaction import AFMLayer, FM
from ..layers.utils import concat_func, add_func
[docs]def AFM(linear_feature_columns, dnn_feature_columns, fm_group=DEFAULT_GROUP_NAME, use_attention=True,
attention_factor=8,
l2_reg_linear=1e-5, l2_reg_embedding=1e-5, l2_reg_att=1e-5, afm_dropout=0, seed=1024,
task='binary'):
"""Instantiates the Attentional Factorization Machine architecture.
:param linear_feature_columns: An iterable containing all the features used by linear part of the model.
:param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
:param fm_group: list, group_name of features that will be used to do feature interactions.
:param use_attention: bool,whether use attention or not,if set to ``False``.it is the same as **standard Factorization Machine**
:param attention_factor: positive integer,units in attention net
:param l2_reg_linear: float. L2 regularizer strength applied to linear part
:param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
:param l2_reg_att: float. L2 regularizer strength applied to attention net
:param afm_dropout: float in [0,1), Fraction of the attention net output units to dropout.
:param seed: integer ,to use as random seed.
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
features = build_input_features(
linear_feature_columns + dnn_feature_columns)
inputs_list = list(features.values())
group_embedding_dict, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding,
seed, support_dense=False, support_group=True)
linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear',
l2_reg=l2_reg_linear)
if use_attention:
fm_logit = add_func([AFMLayer(attention_factor, l2_reg_att, afm_dropout,
seed)(list(v)) for k, v in group_embedding_dict.items() if k in fm_group])
else:
fm_logit = add_func([FM()(concat_func(v, axis=1))
for k, v in group_embedding_dict.items() if k in fm_group])
final_logit = add_func([linear_logit, fm_logit])
output = PredictionLayer(task)(final_logit)
model = Model(inputs=inputs_list, outputs=output)
return model