deepctr_torch/models/wdl.py

# -*- coding:utf-8 -*-
"""
Author:
    Weichen Shen,weichenswc@163.com
Reference:
    [1] Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.(https://arxiv.org/pdf/1606.07792.pdf)
"""

import torch.nn as nn

from .basemodel import BaseModel
from ..inputs import combined_dnn_input
from ..layers import DNN


class WDL(BaseModel):
    """Instantiates the Wide&Deep Learning 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 DNN
    :param l2_reg_linear: float. L2 regularizer strength applied to wide 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, dnn_hidden_units=(256, 128),
                 l2_reg_linear=1e-5,
                 l2_reg_embedding=1e-5, 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(WDL, 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.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.to(device)

    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)

        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)
            logit += dnn_logit

        y_pred = self.out(logit)

        return y_pred