# -*- coding:utf-8 -*-
"""
Author:
Wutong Zhang
Reference:
[1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247)
"""
import torch
import torch.nn as nn
from .basemodel import BaseModel
from ..inputs import combined_dnn_input
from ..layers import DNN, CIN
[docs]class xDeepFM(BaseModel):
"""Instantiates the xDeepFM 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 dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net
:param cin_layer_size: list,list of positive integer or empty list, the feature maps in each hidden layer of Compressed Interaction Network
:param cin_split_half: bool.if set to True, half of the feature maps in each hidden will connect to output unit
:param cin_activation: activation function used on feature maps
:param l2_reg_linear: float. L2 regularizer strength applied to linear part
:param l2_reg_embedding: L2 regularizer strength applied to embedding vector
:param l2_reg_dnn: L2 regularizer strength applied to deep net
:param l2_reg_cin: L2 regularizer strength applied to CIN.
: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 dnn_use_bn: bool. Whether use BatchNormalization before activation or not 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, dnn_hidden_units=(256, 256),
cin_layer_size=(256, 128,), cin_split_half=True, cin_activation='relu', l2_reg_linear=0.00001,
l2_reg_embedding=0.00001, l2_reg_dnn=0, l2_reg_cin=0, init_std=0.0001, seed=1024, dnn_dropout=0,
dnn_activation='relu', dnn_use_bn=False, task='binary', device='cpu', gpus=None):
super(xDeepFM, 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.dnn_hidden_units = dnn_hidden_units
self.use_dnn = len(dnn_feature_columns) > 0 and len(dnn_hidden_units) > 0
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.dnn_linear = nn.Linear(dnn_hidden_units[-1], 1, bias=False).to(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_dnn)
self.cin_layer_size = cin_layer_size
self.use_cin = len(self.cin_layer_size) > 0 and len(dnn_feature_columns) > 0
if self.use_cin:
field_num = len(self.embedding_dict)
if cin_split_half == True:
self.featuremap_num = sum(
cin_layer_size[:-1]) // 2 + cin_layer_size[-1]
else:
self.featuremap_num = sum(cin_layer_size)
self.cin = CIN(field_num, cin_layer_size,
cin_activation, cin_split_half, l2_reg_cin, seed, device=device)
self.cin_linear = nn.Linear(self.featuremap_num, 1, bias=False).to(device)
self.add_regularization_weight(filter(lambda x: 'weight' in x[0], self.cin.named_parameters()),
l2=l2_reg_cin)
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)
linear_logit = self.linear_model(X)
if self.use_cin:
cin_input = torch.cat(sparse_embedding_list, dim=1)
cin_output = self.cin(cin_input)
cin_logit = self.cin_linear(cin_output)
if self.use_dnn:
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
dnn_output = self.dnn(dnn_input)
dnn_logit = self.dnn_linear(dnn_output)
if len(self.dnn_hidden_units) == 0 and len(self.cin_layer_size) == 0: # only linear
final_logit = linear_logit
elif len(self.dnn_hidden_units) == 0 and len(self.cin_layer_size) > 0: # linear + CIN
final_logit = linear_logit + cin_logit
elif len(self.dnn_hidden_units) > 0 and len(self.cin_layer_size) == 0: # linear +　Deep
final_logit = linear_logit + dnn_logit
elif len(self.dnn_hidden_units) > 0 and len(self.cin_layer_size) > 0: # linear + CIN + Deep
final_logit = linear_logit + dnn_logit + cin_logit
else:
raise NotImplementedError
y_pred = self.out(final_logit)
return y_pred