# -*- coding:utf-8 -*-
"""
Author:
chen_kkkk, bgasdo36977@gmail.com
zanshuxun, zanshuxun@aliyun.com
Reference:
[1] Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12. (https://arxiv.org/abs/1708.05123)
[2] Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020. (https://arxiv.org/abs/2008.13535)
"""
import torch
import torch.nn as nn
from .basemodel import BaseModel
from ..inputs import combined_dnn_input
from ..layers import CrossNet, DNN
[docs]class DCN(BaseModel):
"""Instantiates the Deep&Cross Network architecture. Including DCN-V (parameterization='vector')
and DCN-M (parameterization='matrix').
: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 cross_num: positive integet,cross layer number
:param cross_parameterization: str, ``"vector"`` or ``"matrix"``, how to parameterize the cross network.
:param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN
:param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
:param l2_reg_cross: float. L2 regularizer strength applied to cross net
: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_use_bn: bool. Whether use BatchNormalization before activation or not DNN
: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, cross_num=2, cross_parameterization='vector',
dnn_hidden_units=(128, 128), l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_cross=0.00001,
l2_reg_dnn=0, init_std=0.0001, seed=1024, dnn_dropout=0, dnn_activation='relu', dnn_use_bn=False,
task='binary', device='cpu', gpus=None):
super(DCN, self).__init__(linear_feature_columns=linear_feature_columns,
dnn_feature_columns=dnn_feature_columns, l2_reg_embedding=l2_reg_embedding,
init_std=init_std, seed=seed, task=task, device=device, gpus=gpus)
self.dnn_hidden_units = dnn_hidden_units
self.cross_num = cross_num
self.dnn = DNN(self.compute_input_dim(dnn_feature_columns), dnn_hidden_units,
activation=dnn_activation, use_bn=dnn_use_bn, l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout,
init_std=init_std, device=device)
if len(self.dnn_hidden_units) > 0 and self.cross_num > 0:
dnn_linear_in_feature = self.compute_input_dim(dnn_feature_columns) + dnn_hidden_units[-1]
elif len(self.dnn_hidden_units) > 0:
dnn_linear_in_feature = dnn_hidden_units[-1]
elif self.cross_num > 0:
dnn_linear_in_feature = self.compute_input_dim(dnn_feature_columns)
self.dnn_linear = nn.Linear(dnn_linear_in_feature, 1, bias=False).to(
device)
self.crossnet = CrossNet(in_features=self.compute_input_dim(dnn_feature_columns),
layer_num=cross_num, parameterization=cross_parameterization, 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.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_linear)
self.add_regularization_weight(self.crossnet.kernels, l2=l2_reg_cross)
self.to(device)
[docs] def forward(self, X):
logit = self.linear_model(X)
sparse_embedding_list, dense_value_list = self.input_from_feature_columns(X, self.dnn_feature_columns,
self.embedding_dict)
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
if len(self.dnn_hidden_units) > 0 and self.cross_num > 0: # Deep & Cross
deep_out = self.dnn(dnn_input)
cross_out = self.crossnet(dnn_input)
stack_out = torch.cat((cross_out, deep_out), dim=-1)
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.cross_num > 0: # Only Cross
cross_out = self.crossnet(dnn_input)
logit += self.dnn_linear(cross_out)
else: # Error
pass
y_pred = self.out(logit)
return y_pred