An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree的代码

基于图社区神经网络（Graph Community Neural Network, GCN）和时间序列模糊决策树（Time-Series Fuzzy Decision Tree, TSFDT）的可解释性强的列车延误预测模型是一种结合了深度学习和传统机器学习技术的复杂算法。这类模型通常用于交通领域的应用，如铁路运输系统的实时调度优化。

其核心代码可能会包括以下几个部分：

1. **数据预处理**:

import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# 加载数据
data = pd.read_csv('train_data.csv')
scaler = MinMaxScaler()

# 归一化时间序列特征
data[['delay', 'weather', ...]] = scaler.fit_transform(data[['delay', 'weather', ...]])

2. **构建图社区神经网络**:

import torch_geometric.nn as pyg_nn
import torch_geometric.data

def build_gcn_layer():
    return pyg_nn.GraphConv(..., add_self_loops=True)

model = Sequential(
    build_gcn_layer(),  # GCN层
    ReLU(),
    build_gcn_layer(),  # 可能还有其他GCN层
)

3. **时间序列模糊决策树组件**:

from tsfuzzy import FuzzyDecisionTreeRegressor

tree = FuzzyDecisionTreeRegressor(...)

4. **集成模型**:

class IntegratedModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.gcn = model
        self.tsft = tree

    def forward(self, x, graph):
        gcn_output = self.gcn(x, graph)
        tsft_input = gcn_output.view(-1, ...)
        delay_pred = self.tsft(tsft_input)
        return delay_pred

5. **训练和预测**:

optimizer = Adam(model.parameters(), lr=0.01)
for epoch in range(num_epochs):
    train_loss = train_model(model, data_train, optimizer)
    validate_loss = evaluate_model(model, data_val)
    
# 预测阶段
with torch.no_grad():
    test_delay_pred = integrated_model(test_data.x, test_data.edge_index)