# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,weichenswc@163.com
Reference:
[1] Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.(https://arxiv.org/abs/1810.11921)
"""
import torch
import torch.nn as nn
from .basemodel import BaseModel
from ..inputs import combined_dnn_input
from ..layers import DNN, concat_fun, InteractingLayer
[docs]class AutoInt(BaseModel):
"""Instantiates the AutoInt Network 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 att_layer_num: int.The InteractingLayer number to be used.
:param att_head_num: int.The head number in multi-head self-attention network.
:param att_res: bool.Whether or not use standard residual connections before output.
:param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN
:param dnn_activation: Activation function to use in DNN
:param l2_reg_dnn: float. L2 regularizer strength applied to DNN
:param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
:param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param init_std: float,to use as the initialize std of embedding vector
:param seed: integer ,to use as random seed.
: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, att_layer_num=3,
att_head_num=2, att_res=True, dnn_hidden_units=(256, 128), dnn_activation='relu',
l2_reg_dnn=0, l2_reg_embedding=1e-5, dnn_use_bn=False, dnn_dropout=0, init_std=0.0001, seed=1024,
task='binary', device='cpu', gpus=None):
super(AutoInt, self).__init__(linear_feature_columns, dnn_feature_columns, l2_reg_linear=0,
l2_reg_embedding=l2_reg_embedding, init_std=init_std, seed=seed, task=task,
device=device, gpus=gpus)
if len(dnn_hidden_units) <= 0 and att_layer_num <= 0:
raise ValueError("Either hidden_layer or att_layer_num must > 0")
self.use_dnn = len(dnn_feature_columns) > 0 and len(dnn_hidden_units) > 0
field_num = len(self.embedding_dict)
embedding_size = self.embedding_size
if len(dnn_hidden_units) and att_layer_num > 0:
dnn_linear_in_feature = dnn_hidden_units[-1] + field_num * embedding_size
elif len(dnn_hidden_units) > 0:
dnn_linear_in_feature = dnn_hidden_units[-1]
elif att_layer_num > 0:
dnn_linear_in_feature = field_num * embedding_size
else:
raise NotImplementedError
self.dnn_linear = nn.Linear(dnn_linear_in_feature, 1, bias=False).to(device)
self.dnn_hidden_units = dnn_hidden_units
self.att_layer_num = att_layer_num
if self.use_dnn:
self.dnn = DNN(self.compute_input_dim(dnn_feature_columns), dnn_hidden_units,
activation=dnn_activation, l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout, use_bn=dnn_use_bn,
init_std=init_std, device=device)
self.add_regularization_weight(
filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
self.int_layers = nn.ModuleList(
[InteractingLayer(embedding_size, att_head_num, att_res, device=device) for _ in range(att_layer_num)])
self.to(device)
[docs] def forward(self, X):
sparse_embedding_list, dense_value_list = self.input_from_feature_columns(X, self.dnn_feature_columns,
self.embedding_dict)
logit = self.linear_model(X)
att_input = concat_fun(sparse_embedding_list, axis=1)
for layer in self.int_layers:
att_input = layer(att_input)
att_output = torch.flatten(att_input, start_dim=1)
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
if len(self.dnn_hidden_units) > 0 and self.att_layer_num > 0: # Deep & Interacting Layer
deep_out = self.dnn(dnn_input)
stack_out = concat_fun([att_output, deep_out])
logit += self.dnn_linear(stack_out)
elif len(self.dnn_hidden_units) > 0: # Only Deep
deep_out = self.dnn(dnn_input)
logit += self.dnn_linear(deep_out)
elif self.att_layer_num > 0: # Only Interacting Layer
logit += self.dnn_linear(att_output)
else: # Error
pass
y_pred = self.out(logit)
return y_pred