# -*- coding:utf-8 -*-
"""
Author:
Wutong Zhang
Weichen Shen,weichenswc@163.com
Reference:
[1] Gai K, Zhu X, Li H, et al. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction[J]. arXiv preprint arXiv:1704.05194, 2017.(https://arxiv.org/abs/1704.05194)
"""
import torch
import torch.nn as nn
from .basemodel import Linear, BaseModel
from ..inputs import build_input_features
from ..layers import PredictionLayer
[docs]class MLR(BaseModel):
"""Instantiates the Mixed Logistic Regression/Piece-wise Linear Model.
:param region_feature_columns: An iterable containing all the features used by region part of the model.
:param base_feature_columns: An iterable containing all the features used by base part of the model.
:param region_num: integer > 1,indicate the piece number
:param l2_reg_linear: float. L2 regularizer strength applied to weight
: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 bias_feature_columns: An iterable containing all the features used by bias part of the model.
: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, region_feature_columns, base_feature_columns=None, bias_feature_columns=None,
region_num=4, l2_reg_linear=1e-5, init_std=0.0001, seed=1024, task='binary', device='cpu', gpus=None
):
super(MLR, self).__init__(region_feature_columns, region_feature_columns, task=task, device=device, gpus=gpus)
if region_num <= 1:
raise ValueError("region_num must > 1")
self.l2_reg_linear = l2_reg_linear
self.init_std = init_std
self.seed = seed
self.device = device
self.region_num = region_num
self.region_feature_columns = region_feature_columns
self.base_feature_columns = base_feature_columns
self.bias_feature_columns = bias_feature_columns
if base_feature_columns is None or len(base_feature_columns) == 0:
self.base_feature_columns = region_feature_columns
if bias_feature_columns is None:
self.bias_feature_columns = []
self.feature_index = build_input_features(
self.region_feature_columns + self.base_feature_columns + self.bias_feature_columns)
self.region_linear_model = nn.ModuleList([Linear(
self.region_feature_columns, self.feature_index, self.init_std, self.device) for i in
range(self.region_num)])
self.base_linear_model = nn.ModuleList([Linear(
self.base_feature_columns, self.feature_index, self.init_std, self.device) for i in range(self.region_num)])
if self.bias_feature_columns is not None and len(self.bias_feature_columns) > 0:
self.bias_model = nn.Sequential(
Linear(self.bias_feature_columns, self.feature_index,
self.init_std, self.device),
PredictionLayer(task='binary', use_bias=False))
self.prediction_layer = PredictionLayer(task=task, use_bias=False)
self.to(self.device)
def get_region_score(self, inputs, region_number):
region_logit = torch.cat([self.region_linear_model[i](
inputs) for i in range(region_number)], dim=-1)
region_score = nn.Softmax(dim=-1)(region_logit)
return region_score
def get_learner_score(self, inputs, region_number):
learner_score = self.prediction_layer(torch.cat(
[self.region_linear_model[i](inputs) for i in range(region_number)], dim=-1))
return learner_score
[docs] def forward(self, X):
region_score = self.get_region_score(X, self.region_num)
learner_score = self.get_learner_score(X, self.region_num)
final_logit = torch.sum(
region_score * learner_score, dim=-1, keepdim=True)
if self.bias_feature_columns is not None and len(self.bias_feature_columns) > 0:
bias_score = self.bias_model(X)
final_logit = final_logit * bias_score
return final_logit