利用pytorch实现推荐算法AFM

AFM（Attentional Factorization Machines）是一种结合了因子分解机（Factorization Machines）和注意力机制（Attention Mechanism）的推荐算法。它的目的是在因子分解机的基础上引入注意力机制，从而提高推荐系统的效果。下面是利用 PyTorch 实现 AFM 的基本步骤：

首先，需要导入所需的库和数据集。例如：

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from sklearn.model_selection import train_test_split
import pandas as pd

# 读取数据集
data = pd.read_csv('data.csv')

然后，需要对数据进行预处理，包括将类别特征进行 one-hot 编码、将数值特征进行归一化等。例如：

# 对类别特征进行 one-hot 编码
cat_cols = ['user_id', 'item_id', 'genre']
for col in cat_cols:
    data[col] = data[col].astype('category')
data = pd.get_dummies(data, columns=cat_cols)

# 对数值特征进行归一化
num_cols = ['age', 'rating']
for col in num_cols:
    min_val = data[col].min()
    max_val = data[col].max()
    data[col] = (data[col] - min_val) / (max_val - min_val)

接下来，需要将数据集划分为训练集和测试集，并将其转换为 PyTorch 的 Dataset 和 DataLoader。例如：

# 划分训练集和测试集
train_data, test_data = train_test_split(data, test_size=0.2)

# 将数据集转换为 PyTorch 的 Dataset
class AFMDataset(Dataset):
    def __init__(self, data):
        self.X = torch.tensor(data.drop('rating', axis=1).values, dtype=torch.float32)
        self.y = torch.tensor(data['rating'].values, dtype=torch.float32)

    def __len__(self):
        return len(self.X)

    def __getitem__(self, idx):
        return self.X[idx], self.y[idx]

train_dataset = AFMDataset(train_data)
test_dataset = AFMDataset(test_data)

# 将数据集转换为 PyTorch 的 DataLoader
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

然后，需要定义 AFM 模型。AFM 模型由两个部分组成：FM 层和注意力层。FM 层用于学习特征之间的交互关系，注意力层用于计算每个特征的权重。例如：

class FM(nn.Module):
    def __init__(self, num_features):
        super().__init__()
        self.num_features = num_features
        self.w = nn.Parameter(torch.randn(num_features))
        self.bias = nn.Parameter(torch.randn(1))

    def forward(self, x):
        interactions = torch.sum(torch.mm(x, x.t()) * self.w, dim=1)
        return interactions + self.bias

class Attention(nn.Module):
    def __init__(self, num_features):
        super().__init__()
        self.num_features = num_features
        self.W = nn.Parameter(torch.randn(num_features, num_features))
        self.bias = nn.Parameter(torch.randn(1))

    def forward(self, x):
        scores = torch.mm(x, self.W)
        scores = torch.mm(scores, x.t())
        scores = nn.functional.softmax(scores, dim=1)
        outputs = torch.mm(scores, x)
        return outputs + self.bias

class AFM(nn.Module):
    def __init__(self, num_features):
        super().__init__()
        self.fm = FM(num_features)
        self.attention = Attention(num_features)

    def forward(self, x):
        fm_output = self.fm(x)
        attention_output = self.attention(x)
        output = fm_output + attention_output
        return torch.sigmoid(output)

最后，需要定义损失函数和优化器，并训练模型。例如：

# 定义损失函数和优化器
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
model = AFM(num_features=train_data.shape[1]-1)
for epoch in range(10):
    for i, (inputs, targets) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

    # 计算测试集上的准确率
    correct = 0
    total = 0
    with torch.no_grad():
        for inputs, targets in test_loader:
            outputs = model(inputs)
            predicted = torch.round(outputs)
            total += targets.size(0)
            correct += (predicted == targets).sum().item()
    accuracy = correct / total
    print(f'Epoch {epoch+1}, Test Accuracy: {accuracy:.2f}')

通过以上步骤，就可以利用 PyTorch 实现 AFM 推荐算法了。