# -*- coding:utf-8 -*-
"""
Author:
Junyi Huo
Reference:
[1] Yang Y, Xu B, Shen F, et al. Operation-aware Neural Networks for User Response Prediction[J]. arXiv preprint arXiv:1904.12579, 2019. （https://arxiv.org/pdf/1904.12579）
"""
from .basemodel import *
from ..inputs import combined_dnn_input
from ..layers import DNN
class Interac(nn.Module):
def __init__(self, first_size, second_size, emb_size, init_std, sparse=False):
super(Interac, self).__init__()
self.emb1 = nn.Embedding(first_size, emb_size, sparse=sparse)
self.emb2 = nn.Embedding(second_size, emb_size, sparse=sparse)
self.__init_weight(init_std)
def __init_weight(self, init_std):
nn.init.normal_(self.emb1.weight, mean=0, std=init_std)
def forward(self, first, second):
"""
input:
x batch_size * 2
output:
y batch_size * emb_size
"""
first_emb = self.emb1(first)
second_emb = self.emb2(second)
y = first_emb * second_emb # core code
return y
[docs]class ONN(BaseModel):
"""Instantiates the Operation-aware Neural Networks 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 l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
:param l2_reg_linear: float. L2 regularizer strength applied to linear part.
: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 use_bn: bool,whether use bn after ffm out or not
:param reduce_sum: bool,whether apply reduce_sum on cross vector
: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=(128, 128),
l2_reg_embedding=1e-5, l2_reg_linear=1e-5, l2_reg_dnn=0,
dnn_dropout=0, init_std=0.0001, seed=1024, dnn_use_bn=False, dnn_activation='relu',
task='binary', device='cpu', gpus=None):
super(ONN, 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)
# second order part
embedding_size = self.embedding_size
self.second_order_embedding_dict = self.__create_second_order_embedding_matrix(
dnn_feature_columns, embedding_size=embedding_size, sparse=False).to(device)
# add regularization for second_order_embedding
self.add_regularization_weight(self.second_order_embedding_dict.parameters(), l2=l2_reg_embedding)
dim = self.__compute_nffm_dnn_dim(
feature_columns=dnn_feature_columns, embedding_size=embedding_size)
self.dnn = DNN(inputs_dim=dim,
hidden_units=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.to(device)
def __compute_nffm_dnn_dim(self, feature_columns, embedding_size):
sparse_feature_columns = list(
filter(lambda x: isinstance(x, SparseFeat), feature_columns)) if len(feature_columns) else []
dense_feature_columns = list(
filter(lambda x: isinstance(x, DenseFeat), feature_columns)) if len(feature_columns) else []
return int(len(sparse_feature_columns) * (len(sparse_feature_columns) - 1) / 2 * embedding_size +
sum(map(lambda x: x.dimension, dense_feature_columns)))
def __input_from_second_order_column(self, X, feature_columns, second_order_embedding_dict):
'''
:param X: same as input_from_feature_columns
:param feature_columns: same as input_from_feature_columns
:param second_order_embedding_dict: ex: {'A1+A2': Interac model} created by function create_second_order_embedding_matrix
:return:
'''
sparse_feature_columns = list(
filter(lambda x: isinstance(x, SparseFeat), feature_columns)) if len(feature_columns) else []
second_order_embedding_list = []
for first_index in range(len(sparse_feature_columns) - 1):
for second_index in range(first_index + 1, len(sparse_feature_columns)):
first_name = sparse_feature_columns[first_index].embedding_name
second_name = sparse_feature_columns[second_index].embedding_name
second_order_embedding_list.append(
second_order_embedding_dict[first_name + "+" + second_name](
X[:, self.feature_index[first_name][0]
:self.feature_index[first_name][1]].long(),
X[:, self.feature_index[second_name][0]
:self.feature_index[second_name][1]].long()
)
)
return second_order_embedding_list
def __create_second_order_embedding_matrix(self, feature_columns, embedding_size, init_std=0.0001, sparse=False):
sparse_feature_columns = list(
filter(lambda x: isinstance(x, SparseFeat), feature_columns)) if len(feature_columns) else []
temp_dict = {}
for first_index in range(len(sparse_feature_columns) - 1):
for second_index in range(first_index + 1, len(sparse_feature_columns)):
first_name = sparse_feature_columns[first_index].embedding_name
second_name = sparse_feature_columns[second_index].embedding_name
temp_dict[first_name + "+" + second_name] = Interac(sparse_feature_columns[first_index].vocabulary_size,
sparse_feature_columns[
second_index].vocabulary_size,
emb_size=embedding_size,
init_std=init_std,
sparse=sparse)
return nn.ModuleDict(temp_dict)
[docs] def forward(self, X):
_, dense_value_list = self.input_from_feature_columns(X, self.dnn_feature_columns,
self.embedding_dict)
linear_logit = self.linear_model(X)
spare_second_order_embedding_list = self.__input_from_second_order_column(X, self.dnn_feature_columns,
self.second_order_embedding_dict)
dnn_input = combined_dnn_input(
spare_second_order_embedding_list, dense_value_list)
dnn_output = self.dnn(dnn_input)
dnn_logit = self.dnn_linear(dnn_output)
if len(self.dnn_feature_columns) > 0:
final_logit = dnn_logit + linear_logit
else:
final_logit = linear_logit
y_pred = self.out(final_logit)
return y_pred