# -*- coding:utf-8 -*-
"""
Author:
Weiyu Cheng, weiyu_cheng@sjtu.edu.cn
Reference:
[1] Cheng, W., Shen, Y. and Huang, L. 2020. Adaptive Factorization Network: Learning Adaptive-Order Feature
Interactions. Proceedings of the AAAI Conference on Artificial Intelligence. 34, 04 (Apr. 2020), 3609-3616.
"""
import torch
import torch.nn as nn
from .basemodel import BaseModel
from ..layers import LogTransformLayer, DNN
[docs]class AFN(BaseModel):
"""Instantiates the Adaptive Factorization Network architecture.
In DeepCTR-Torch, we only provide the non-ensembled version of AFN for the consistency of model interfaces. For the ensembled version of AFN+, please refer to https://github.com/WeiyuCheng/DeepCTR-Torch (Pytorch Version) or https://github.com/WeiyuCheng/AFN-AAAI-20 (Tensorflow Version).
: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 ltl_hidden_size: integer, the number of logarithmic neurons in AFN
:param afn_dnn_hidden_units: list, list of positive integer or empty list, the layer number and units in each layer of DNN layers in AFN
: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_dnn: float. L2 regularizer strength applied to DNN
:param init_std: float,to use as the initialize std of embedding vector
:param seed: integer ,to use as random seed.
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param dnn_activation: Activation function to use in DNN
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:param device: str, ``"cpu"`` or ``"cuda:0"``
:param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
:return: A PyTorch model instance.
"""
def __init__(self,
linear_feature_columns, dnn_feature_columns,
ltl_hidden_size=256, afn_dnn_hidden_units=(256, 128),
l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0,
init_std=0.0001, seed=1024, dnn_dropout=0, dnn_activation='relu',
task='binary', device='cpu', gpus=None):
super(AFN, self).__init__(linear_feature_columns, dnn_feature_columns, l2_reg_linear=l2_reg_linear,
l2_reg_embedding=l2_reg_embedding, init_std=init_std, seed=seed, task=task,
device=device, gpus=gpus)
self.ltl = LogTransformLayer(len(self.embedding_dict), self.embedding_size, ltl_hidden_size)
self.afn_dnn = DNN(self.embedding_size * ltl_hidden_size, afn_dnn_hidden_units,
activation=dnn_activation, l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout, use_bn=True,
init_std=init_std, device=device)
self.afn_dnn_linear = nn.Linear(afn_dnn_hidden_units[-1], 1)
self.to(device)
[docs] def forward(self, X):
sparse_embedding_list, _ = self.input_from_feature_columns(X, self.dnn_feature_columns,
self.embedding_dict)
logit = self.linear_model(X)
if len(sparse_embedding_list) == 0:
raise ValueError('Sparse embeddings not provided. AFN only accepts sparse embeddings as input.')
afn_input = torch.cat(sparse_embedding_list, dim=1)
ltl_result = self.ltl(afn_input)
afn_logit = self.afn_dnn(ltl_result)
afn_logit = self.afn_dnn_linear(afn_logit)
logit += afn_logit
y_pred = self.out(logit)
return y_pred