Coverage for hopwise/model/context_aware_recommender/lr.py: 100%

1# @Time : 2020/08/30 

2# @Author : Xinyan Fan 

3# @Email : xinyan.fan@ruc.edu.cn 

4# @File : lr.py 

5 

6r"""LR 

7##################################################### 

8Reference: 

9 Matthew Richardson et al. "Predicting Clicks Estimating the Click-Through Rate for New Ads." in WWW 2007. 

10""" 

11 

12from torch import nn 

13from torch.nn.init import xavier_normal_ 

14 

15from hopwise.model.abstract_recommender import ContextRecommender 

16 

17 

18class LR(ContextRecommender): 

19 r"""LR is a context-based recommendation model. 

20 It aims to predict the CTR given a set of features by using logistic regression, 

21 which is ideally suited for probabilities as it always predicts a value between 0 and 1: 

22 

23 .. math:: 

24 CTR = \frac{1}{1+e^{-Z}} 

25 

26 Z = \sum_{i} {w_i}{x_i} 

27 """ 

28 

29 def __init__(self, config, dataset): 

30 super().__init__(config, dataset) 

31 

32 self.sigmoid = nn.Sigmoid() 

33 self.loss = nn.BCEWithLogitsLoss() 

34 

35 # parameters initialization 

36 self.apply(self._init_weights) 

37 

38 def _init_weights(self, module): 

39 if isinstance(module, nn.Embedding): 

40 xavier_normal_(module.weight.data) 

41 

42 def forward(self, interaction): 

43 output = self.first_order_linear(interaction) 

44 return output.squeeze(-1) 

45 

46 def calculate_loss(self, interaction): 

47 label = interaction[self.LABEL] 

48 

49 output = self.forward(interaction) 

50 return self.loss(output, label) 

51 

52 def predict(self, interaction): 

53 return self.sigmoid(self.forward(interaction))