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# -*- coding: utf-8 -*- | ||
# @Time : 2020/7/8 10:33 | ||
# @Author : Shanlei Mu | ||
# @Email : slmu@ruc.edu.cn | ||
# @File : deepfm.py | ||
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""" | ||
Reference: | ||
Huifeng Guo et al., "DeepFM: A Factorization-Machine based Neural Network for CTR Prediction." in IJCAI 2017. | ||
""" | ||
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import torch | ||
import torch.nn as nn | ||
import numpy as np | ||
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from model.abstract_recommender import AbstractRecommender | ||
from model.layers import FMEmbedding, FMFirstOrderLinear, BaseFactorizationMachine, MLPLayers | ||
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class DeepFM(AbstractRecommender): | ||
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def __init__(self, config, dataset): | ||
super(DeepFM).__init__() | ||
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self.embedding_size = config['model.embedding_size'] | ||
self.layers = config['model.layers'] | ||
self.dropout = config['model.dropout'] | ||
self.field_names = list(dataset.token2id.keys()) | ||
self.field_dims = [len(dataset.token2id[v]) for v in self.field_names] | ||
self.field_seqlen = [dataset.token2seqlen[v] for v in self.field_names] | ||
self.offsets = self._build_offsets() | ||
self.layers = [self.embedding_size * len(self.field_names)] + self.layers | ||
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self.first_order_linear = FMFirstOrderLinear(self.filed_dims, self.offsets) | ||
self.embedding = FMEmbedding(self.filed_dims, self.offsets, self.embedding_size) | ||
self.fm = BaseFactorizationMachine(reduce_sum=True) | ||
self.mlp_layers = MLPLayers(self.layers, self.dropout) | ||
self.deep_predict_layer = nn.Linear(self.layers[-1], 1) | ||
self.sigmoid = nn.Sigmoid() | ||
self.loss = nn.BCELoss() | ||
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def _build_offsets(self): | ||
offsets = [] | ||
for i in range(len(self.field_names)): | ||
offsets += [self.field_dims[i]] | ||
offsets += [0] * (self.field_seqlen[i] - 1) | ||
offsets = np.array((0, *np.cumsum(offsets)[:-1]), dtype=np.long) | ||
return offsets | ||
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def forward(self, interaction): | ||
x = [] | ||
for field in self.field_names: | ||
x.append(interaction[field]) | ||
x = torch.cat(x, dim=1) | ||
embed_x = self.embedding(x) | ||
y_fm = self.first_order_linear(x) + self.fm(embed_x) | ||
# todo: how to deal with multi-hot feature (原论文明确规定每个field都是one-hot feature) | ||
y_deep = self.deep_predict_layer( | ||
self.mlp_layers(embed_x.view(-1, sum(self.field_seqlen) * self.embedding_size))) | ||
y = self.sigmoid(y_fm + y_deep) | ||
return y | ||
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def train_model(self, interaction): | ||
label = interaction[LABEL] | ||
output = self.forward(interaction) | ||
return self.loss(output, label) | ||
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def predict(self, interaction): | ||
return self.forward(interaction) |
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# -*- coding: utf-8 -*- | ||
# @Time : 2020/7/8 10:09 | ||
# @Author : Shanlei Mu | ||
# @Email : slmu@ruc.edu.cn | ||
# @File : fm.py | ||
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""" | ||
Reference: | ||
Steffen Rendle et al., "Factorization Machines." in ICDM 2010. | ||
""" | ||
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import torch | ||
import torch.nn as nn | ||
import numpy as np | ||
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from model.abstract_recommender import AbstractRecommender | ||
from model.layers import FMEmbedding, FMFirstOrderLinear, BaseFactorizationMachine | ||
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class FM(AbstractRecommender): | ||
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def __init__(self, config, dataset): | ||
super(FM).__init__() | ||
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self.embedding_size = config['model.embedding_size'] | ||
self.field_names = list(dataset.token2id.keys()) | ||
self.field_dims = [len(dataset.token2id[v]) for v in self.field_names] | ||
self.field_seqlen = [dataset.token2seqlen[v] for v in self.field_names] | ||
self.offsets = self._build_offsets() | ||
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self.embedding = FMEmbedding(self.filed_dims, self.offsets, self.embedding_size) | ||
self.first_order_linear = FMFirstOrderLinear(self.filed_dims, self.offsets) | ||
self.fm = BaseFactorizationMachine(reduce_sum=True) | ||
self.sigmoid = nn.Sigmoid() | ||
self.loss = nn.BCELoss() | ||
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def _build_offsets(self): | ||
offsets = [] | ||
for i in range(len(self.field_names)): | ||
offsets += [self.field_dims[i]] | ||
offsets += [0] * (self.field_seqlen[i] - 1) | ||
offsets = np.array((0, *np.cumsum(offsets)[:-1]), dtype=np.long) | ||
return offsets | ||
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def forward(self, interaction): | ||
x = [] | ||
for field in self.field_names: | ||
x.append(interaction[field]) | ||
x = torch.cat(x, dim=1) | ||
y = self.sigmoid(self.first_order_linear(x) + self.fm(self.embedding(x))) | ||
return y | ||
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def train_model(self, interaction): | ||
label = interaction[LABEL] | ||
output = self.forward(interaction) | ||
return self.loss(output, label) | ||
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def predict(self, interaction): | ||
return self.forward(interaction) |
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