推荐系统和广告CTR预估主流模型的演化有两条主要路线。
第一条是显式建模特征交互，提升模型对交叉特征的捕获能力。(如Wide&Deep,PNN,FNN,DCN,DeepFM,AutoInt等)
第二条是加入注意力机制，提升模型的自适应能力和解释性。(如DIN,DIEN,DSIN,FiBiNET,AutoInt等)
在所有这些模型中，DeepFM属于性价比非常高的模型（结构简洁，计算高效，指标有竞争力）。
张俊林大佬 在2019年的时候甚至建议 沿着 LR->FM->DeepFM->干点别的 这样的路线去迭代推荐系统。
参考文档：
- 《推荐系统CTR预估学习路线》：https://zhuanlan.zhihu.com/p/351078721
- criteo数据集榜单：https://paperswithcode.com/dataset/criteo
- DeepFM论文：https://arxiv.org/abs/1703.04247
- 《清晰易懂，基于pytorch的DeepFM的完整实验代码》：https://zhuanlan.zhihu.com/p/332786045
- torch实现参考：https://github.com/rixwew/pytorch-fm/blob/master/torchfm/model/dfm.py
公众号后台回复关键词：DeepFM，获取本文全部源代码和criteo数据集。
一，DeepFM原理解析
DeepFM继承了DeepWide的主体结构，将高低特征进行融合。
其主要创新点有2个。
一是将Wide部分替换成了 FM结构，以更有效的捕获特征交互interaction.
二是FM中的隐向量 和 Deep部分的 embedding 向量共享权重，减少模型复杂性。
二，DeepFM的pytorch实现
下面是DeepFM的一个pytorch实现。
除了添加了一个并行的MLP模块用于捕获隐式高阶交叉和组合特征外，其余结构基本和FM的实现完全一致。
代码语言：javascript复制import torch
from torch import nn
from torch import nn,Tensor
import torch.nn.functional as F
class NumEmbedding(nn.Module):
"""
连续特征用linear层编码
输入shape: [batch_size,features_num(n), d_in], # d_in 通常是1
输出shape: [batch_size,features_num(n), d_out]
"""
def __init__(self, n: int, d_in: int, d_out: int, bias: bool = False) -> None:
super().__init__()
self.weight = nn.Parameter(Tensor(n, d_in, d_out))
self.bias = nn.Parameter(Tensor(n, d_out)) if bias else None
with torch.no_grad():
for i in range(n):
layer = nn.Linear(d_in, d_out)
self.weight[i] = layer.weight.T
if self.bias is not None:
self.bias[i] = layer.bias
def forward(self, x_num):
# x_num: batch_size, features_num, d_in
assert x_num.ndim == 3
#x = x_num[..., None] * self.weight[None]
#x = x.sum(-2)
x = torch.einsum("bfi,fij->bfj",x_num,self.weight)
if self.bias is not None:
x = x self.bias[None]
return x
class CatEmbedding(nn.Module):
"""
离散特征用Embedding层编码
输入shape: [batch_size,features_num],
输出shape: [batch_size,features_num, d_embed]
"""
def __init__(self, categories, d_embed):
super().__init__()
self.embedding = torch.nn.Embedding(sum(categories), d_embed)
self.offsets = nn.Parameter(
torch.tensor([0] categories[:-1]).cumsum(0),requires_grad=False)
torch.nn.init.xavier_uniform_(self.embedding.weight.data)
def forward(self, x_cat):
"""
:param x_cat: Long tensor of size ``(batch_size, features_num)``
"""
x = x_cat self.offsets[None]
return self.embedding(x)
class CatLinear(nn.Module):
"""
离散特征用Embedding实现线性层（等价于先F.onehot再nn.Linear()）
输入shape: [batch_size,features_num],
输出shape: [batch_size,features_num, d_out]
"""
def __init__(self, categories, d_out=1):
super().__init__()
self.fc = nn.Embedding(sum(categories), d_out)
self.bias = nn.Parameter(torch.zeros((d_out,)))
self.offsets = nn.Parameter(
torch.tensor([0] categories[:-1]).cumsum(0),requires_grad=False)
def forward(self, x_cat):
"""
:param x: Long tensor of size ``(batch_size, features_num)``
"""
x = x_cat self.offsets[None]
return torch.sum(self.fc(x), dim=1) self.bias
class FMLayer(nn.Module):
"""
FM交互项
"""
def __init__(self, reduce_sum=True):
super().__init__()
self.reduce_sum = reduce_sum
def forward(self, x): #注意：这里的x是公式中的 <v_i> * xi
"""
:param x: Float tensor of size ``(batch_size, num_features, k)``
"""
square_of_sum = torch.sum(x, dim=1) ** 2
sum_of_square = torch.sum(x ** 2, dim=1)
ix = square_of_sum - sum_of_square
if self.reduce_sum:
ix = torch.sum(ix, dim=1, keepdim=True)
return 0.5 * ix
#deep部分
class MultiLayerPerceptron(nn.Module):
def __init__(self, d_in, d_layers, dropout,
d_out = 1):
super().__init__()
layers = []
for d in d_layers:
layers.append(nn.Linear(d_in, d))
layers.append(nn.BatchNorm1d(d))
layers.append(nn.ReLU())
layers.append(nn.Dropout(p=dropout))
d_in = d
layers.append(nn.Linear(d_layers[-1], d_out))
self.mlp = nn.Sequential(*layers)
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, d_in)``
"""
return self.mlp(x)
class DeepFM(nn.Module):
"""
DeepFM模型。
"""
def __init__(self, d_numerical, categories, d_embed,
deep_layers, deep_dropout,
n_classes = 1):
super().__init__()
if d_numerical is None:
d_numerical = 0
if categories is None:
categories = []
self.categories = categories
self.n_classes = n_classes
self.num_linear = nn.Linear(d_numerical,n_classes) if d_numerical else None
self.cat_linear = CatLinear(categories,n_classes) if categories else None
self.num_embedding = NumEmbedding(d_numerical,1,d_embed) if d_numerical else None
self.cat_embedding = CatEmbedding(categories, d_embed) if categories else None
if n_classes==1:
self.fm = FMLayer(reduce_sum=True)
self.fm_linear = None
else:
assert n_classes>=2
self.fm = FMLayer(reduce_sum=False)
self.fm_linear = nn.Linear(d_embed,n_classes)
self.deep_in = d_numerical*d_embed len(categories)*d_embed
self.deep = MultiLayerPerceptron(
d_in= self.deep_in,
d_layers = deep_layers,
dropout = deep_dropout,
d_out = n_classes
)
def forward(self, x):
"""
x_num: numerical features
x_cat: category features
"""
x_num,x_cat = x
#linear部分
x = 0.0
if self.num_linear:
x = x self.num_linear(x_num)
if self.cat_linear:
x = x self.cat_linear(x_cat)
#fm部分
x_embedding = []
if self.num_embedding:
x_embedding.append(self.num_embedding(x_num[...,None]))
if self.cat_embedding:
x_embedding.append(self.cat_embedding(x_cat))
x_embedding = torch.cat(x_embedding,dim=1)
if self.n_classes==1:
x = x self.fm(x_embedding)
else:
x = x self.fm_linear(self.fm(x_embedding))
#deep部分
x = x self.deep(x_embedding.view(-1,self.deep_in))
if self.n_classes==1:
x = x.squeeze(-1)
return x
##测试 DeepFM
model = DeepFM(d_numerical = 3, categories = [4,3,2],
d_embed = 4, deep_layers = [20,20], deep_dropout=0.1,
n_classes = 1)
x_num = torch.randn(2,3)
x_cat = torch.randint(0,2,(2,3))
model((x_num,x_cat))
三，criteo数据集完整范例
Criteo数据集是一个经典的广告点击率CTR预测数据集。
这个数据集的目标是通过用户特征和广告特征来预测某条广告是否会为用户点击。
数据集有13维数值特征(I1~I13)和26维类别特征(C14~C39), 共39维特征, 特征中包含着许多缺失值。
训练集4000万个样本，测试集600万个样本。数据集大小超过100G.
此处使用的是采样100万个样本后的cretio_small数据集。
代码语言：javascript复制!pip install -U torchkeras -i https://pypi.org/simple/
import numpy as np
import pandas as pd
import datetime
from sklearn.model_selection import train_test_split
import torch
from torch import nn
from torch.utils.data import Dataset,DataLoader
import torch.nn.functional as F
import torchkeras
def printlog(info):
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("n" "=========="*8 "%s"%nowtime)
print(info '...nn')
1，准备数据
代码语言：javascript复制from sklearn.preprocessing import LabelEncoder,QuantileTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
dfdata = pd.read_csv("../data/criteo_small.zip",sep="t",header=None)
dfdata.columns = ["label"] ["I" str(x) for x in range(1,14)] [
"C" str(x) for x in range(14,40)]
cat_cols = [x for x in dfdata.columns if x.startswith('C')]
num_cols = [x for x in dfdata.columns if x.startswith('I')]
num_pipe = Pipeline(steps = [('impute',SimpleImputer()),('quantile',QuantileTransformer())])
for col in cat_cols:
dfdata[col] = LabelEncoder().fit_transform(dfdata[col])
dfdata[num_cols] = num_pipe.fit_transform(dfdata[num_cols])
categories = [dfdata[col].max() 1 for col in cat_cols]
import torch
from torch.utils.data import Dataset,DataLoader
#DataFrame转换成torch数据集Dataset, 特征分割成X_num,X_cat方式
class DfDataset(Dataset):
def __init__(self,df,
label_col,
num_features,
cat_features,
categories,
is_training=True):
self.X_num = torch.tensor(df[num_features].values).float() if num_features else None
self.X_cat = torch.tensor(df[cat_features].values).long() if cat_features else None
self.Y = torch.tensor(df[label_col].values).float()
self.categories = categories
self.is_training = is_training
def __len__(self):
return len(self.Y)
def __getitem__(self,index):
if self.is_training:
return ((self.X_num[index],self.X_cat[index]),self.Y[index])
else:
return (self.X_num[index],self.X_cat[index])
def get_categories(self):
return self.categories
dftrain_val,dftest = train_test_split(dfdata,test_size=0.2)
dftrain,dfval = train_test_split(dftrain_val,test_size=0.2)
ds_train = DfDataset(dftrain,label_col = "label",num_features = num_cols,cat_features = cat_cols,
categories = categories, is_training=True)
ds_val = DfDataset(dfval,label_col = "label",num_features = num_cols,cat_features = cat_cols,
categories = categories, is_training=True)
ds_test = DfDataset(dftest,label_col = "label",num_features = num_cols,cat_features = cat_cols,
categories = categories, is_training=True)
dl_train = DataLoader(ds_train,batch_size = 2048,shuffle=True)
dl_val = DataLoader(ds_val,batch_size = 2048,shuffle=False)
dl_test = DataLoader(ds_test,batch_size = 2048,shuffle=False)
for features,labels in dl_train:
break
2，定义模型
代码语言：javascript复制def create_net():
net = DeepFM(
d_numerical= ds_train.X_num.shape[1],
categories= ds_train.get_categories(),
d_embed = 8, deep_layers = [128,64,32], deep_dropout=0.25,
n_classes = 1
)
return net
from torchkeras import summary
net = create_net()
print("net:n",net)
summary(net,input_data=features);
net:
DeepFM(
(num_linear): Linear(in_features=13, out_features=1, bias=True)
(cat_linear): CatLinear(
(fc): Embedding(1296709, 1)
)
(num_embedding): NumEmbedding()
(cat_embedding): CatEmbedding(
(embedding): Embedding(1296709, 8)
)
(fm): FMLayer()
(deep): MultiLayerPerceptron(
(mlp): Sequential(
(0): Linear(in_features=312, out_features=128, bias=True)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Dropout(p=0.25, inplace=False)
(4): Linear(in_features=128, out_features=64, bias=True)
(5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU()
(7): Dropout(p=0.25, inplace=False)
(8): Linear(in_features=64, out_features=32, bias=True)
(9): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(10): ReLU()
(11): Dropout(p=0.25, inplace=False)
(12): Linear(in_features=32, out_features=1, bias=True)
)
)
)
--------------------------------------------------------------------------
Layer (type) Output Shape Param #
==========================================================================
Linear-1 [-1, 1] 14
Embedding-2 [-1, 26, 1] 1,296,709
CatLinear-3 [-1, 1] 1,296,736
NumEmbedding-4 [-1, 13, 8] 104
Embedding-5 [-1, 26, 8] 10,373,672
CatEmbedding-6 [-1, 26, 8] 10,373,698
FMLayer-7 [-1, 1] 0
Linear-8 [-1, 128] 40,064
BatchNorm1d-9 [-1, 128] 256
ReLU-10 [-1, 128] 0
Dropout-11 [-1, 128] 0
Linear-12 [-1, 64] 8,256
BatchNorm1d-13 [-1, 64] 128
ReLU-14 [-1, 64] 0
Dropout-15 [-1, 64] 0
Linear-16 [-1, 32] 2,080
BatchNorm1d-17 [-1, 32] 64
ReLU-18 [-1, 32] 0
Dropout-19 [-1, 32] 0
Linear-20 [-1, 1] 33
Sequential-21 [-1, 1] 50,881
MultiLayerPerceptron-22 [-1, 1] 50,881
DeepFM-23 [-1] 11,721,433
==========================================================================
Total params: 35,215,009
Trainable params: 35,214,905
Non-trainable params: 104
--------------------------------------------------------------------------
Input size (MB): 0.000084
Forward/backward pass size (MB): 0.011055
Params size (MB): 134.334599
Estimated Total Size (MB): 134.345737
--------------------------------------------------------------------------
3，训练模型
代码语言：javascript复制import os,sys,time
import numpy as np
import pandas as pd
import datetime
from tqdm import tqdm
import torch
from torch import nn
from accelerate import Accelerator
from copy import deepcopy
def printlog(info):
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("n" "=========="*8 "%s"%nowtime)
print(str(info) "n")
class StepRunner:
def __init__(self, net, loss_fn,stage = "train", metrics_dict = None,
optimizer = None, lr_scheduler = None,
accelerator = None
):
self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage
self.optimizer,self.lr_scheduler = optimizer,lr_scheduler
self.accelerator = accelerator
def __call__(self, features, labels):
#loss
preds = self.net(features)
loss = self.loss_fn(preds,labels)
#backward()
if self.optimizer is not None and self.stage=="train":
if self.accelerator is None:
loss.backward()
else:
self.accelerator.backward(loss)
self.optimizer.step()
if self.lr_scheduler is not None:
self.lr_scheduler.step()
self.optimizer.zero_grad()
#metrics
step_metrics = {self.stage "_" name:metric_fn(preds, labels).item()
for name,metric_fn in self.metrics_dict.items()}
return loss.item(),step_metrics
class EpochRunner:
def __init__(self,steprunner):
self.steprunner = steprunner
self.stage = steprunner.stage
self.steprunner.net.train() if self.stage=="train" else self.steprunner.net.eval()
def __call__(self,dataloader):
total_loss,step = 0,0
loop = tqdm(enumerate(dataloader), total =len(dataloader))
for i, batch in loop:
features,labels = batch
if self.stage=="train":
loss, step_metrics = self.steprunner(features,labels)
else:
with torch.no_grad():
loss, step_metrics = self.steprunner(features,labels)
step_log = dict({self.stage "_loss":loss},**step_metrics)
total_loss = loss
step =1
if i!=len(dataloader)-1:
loop.set_postfix(**step_log)
else:
epoch_loss = total_loss/step
epoch_metrics = {self.stage "_" name:metric_fn.compute().item()
for name,metric_fn in self.steprunner.metrics_dict.items()}
epoch_log = dict({self.stage "_loss":epoch_loss},**epoch_metrics)
loop.set_postfix(**epoch_log)
for name,metric_fn in self.steprunner.metrics_dict.items():
metric_fn.reset()
return epoch_log
class KerasModel(torch.nn.Module):
def __init__(self,net,loss_fn,metrics_dict=None,optimizer=None,lr_scheduler = None):
super().__init__()
self.accelerator = Accelerator()
self.history = {}
self.net = net
self.loss_fn = loss_fn
self.metrics_dict = nn.ModuleDict(metrics_dict)
self.optimizer = optimizer if optimizer is not None else torch.optim.Adam(
self.parameters(), lr=1e-2)
self.lr_scheduler = lr_scheduler
self.net,self.loss_fn,self.metrics_dict,self.optimizer = self.accelerator.prepare(
self.net,self.loss_fn,self.metrics_dict,self.optimizer)
def forward(self, x):
if self.net:
return self.net.forward(x)
else:
raise NotImplementedError
def fit(self, train_data, val_data=None, epochs=10, ckpt_path='checkpoint.pt',
patience=5, monitor="val_loss", mode="min"):
train_data = self.accelerator.prepare(train_data)
val_data = self.accelerator.prepare(val_data) if val_data else []
for epoch in range(1, epochs 1):
printlog("Epoch {0} / {1}".format(epoch, epochs))
# 1，train -------------------------------------------------
train_step_runner = StepRunner(net = self.net,stage="train",
loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),
optimizer = self.optimizer, lr_scheduler = self.lr_scheduler,
accelerator = self.accelerator)
train_epoch_runner = EpochRunner(train_step_runner)
train_metrics = train_epoch_runner(train_data)
for name, metric in train_metrics.items():
self.history[name] = self.history.get(name, []) [metric]
# 2，validate -------------------------------------------------
if val_data:
val_step_runner = StepRunner(net = self.net,stage="val",
loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),
accelerator = self.accelerator)
val_epoch_runner = EpochRunner(val_step_runner)
with torch.no_grad():
val_metrics = val_epoch_runner(val_data)
val_metrics["epoch"] = epoch
for name, metric in val_metrics.items():
self.history[name] = self.history.get(name, []) [metric]
# 3，early-stopping -------------------------------------------------
arr_scores = self.history[monitor]
best_score_idx = np.argmax(arr_scores) if mode=="max" else np.argmin(arr_scores)
if best_score_idx==len(arr_scores)-1:
torch.save(self.net.state_dict(),ckpt_path)
print("<<<<<< reach best {0} : {1} >>>>>>".format(monitor,
arr_scores[best_score_idx]),file=sys.stderr)
if len(arr_scores)-best_score_idx>patience:
print("<<<<<< {} without improvement in {} epoch, early stopping >>>>>>".format(
monitor,patience),file=sys.stderr)
self.net.load_state_dict(torch.load(ckpt_path))
break
return pd.DataFrame(self.history)
@torch.no_grad()
def evaluate(self, val_data):
val_data = self.accelerator.prepare(val_data)
val_step_runner = StepRunner(net = self.net,stage="val",
loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),
accelerator = self.accelerator)
val_epoch_runner = EpochRunner(val_step_runner)
val_metrics = val_epoch_runner(val_data)
return val_metrics
@torch.no_grad()
def predict(self, dataloader):
dataloader = self.accelerator.prepare(dataloader)
result = torch.cat([self.forward(t[0]) for t in dataloader])
return result.data
from torchkeras.metrics import AUC
loss_fn = nn.BCEWithLogitsLoss()
metrics_dict = {"auc":AUC()}
optimizer = torch.optim.Adam(net.parameters(), lr=0.002, weight_decay=0.001)
model = KerasModel(net,
loss_fn = loss_fn,
metrics_dict= metrics_dict,
optimizer = optimizer
)
dfhistory = model.fit(train_data=dl_train,val_data=dl_val,epochs=50, patience=5,
monitor = "val_auc",mode="max",ckpt_path='checkpoint.pt')
可以看到，验证集AUC是0.78029，相比FM模型约涨了一个点左右，good job!
4，评估模型
代码语言：javascript复制%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import matplotlib.pyplot as plt
def plot_metric(dfhistory, metric):
train_metrics = dfhistory["train_" metric]
val_metrics = dfhistory['val_' metric]
epochs = range(1, len(train_metrics) 1)
plt.plot(epochs, train_metrics, 'bo--')
plt.plot(epochs, val_metrics, 'ro-')
plt.title('Training and validation ' metric)
plt.xlabel("Epochs")
plt.ylabel(metric)
plt.legend(["train_" metric, 'val_' metric])
plt.show()
plot_metric(dfhistory,"loss")
plot_metric(dfhistory,"auc")
5，使用模型
代码语言：javascript复制from sklearn.metrics import roc_auc_score
preds = torch.sigmoid(model.predict(dl_val))
labels = torch.cat([x[-1] for x in dl_val])
val_auc = roc_auc_score(labels.cpu().numpy(),preds.cpu().numpy())
print(val_auc)
0.78029
6，保存模型
代码语言：javascript复制torch.save(model.net.state_dict(),"best_deepfm.pt")
net_clone = create_net()
net_clone.load_state_dict(torch.load("best_deepfm.pt"))
from sklearn.metrics import roc_auc_score
net_clone.eval()
preds = torch.cat([torch.sigmoid(net_clone(x[0])).data for x in dl_val])
labels = torch.cat([x[-1] for x in dl_val])
val_auc = roc_auc_score(labels.cpu().numpy(),preds.cpu().numpy())
print(val_auc)
0.78029
DeepFM验证集AUC是0.78029，相比之下FM模型验证集AUC为0.7716，约涨了一个点左右，nice！
以上。
