麻烦提供基于pytorch的EEMD+LightGBM+ConvLSTM的读取csv数据的多输入单输出的时序训练及预测代码

好的，以下是基于PyTorch的EEMD-LightGBM-ConvLSTM的代码示例，用于多输入单输出的时序训练和预测。代码使用PyTorch框架构建ConvLSTM模型，使用LightGBM进行特征选择，以及使用EEMD技术进行数据处理。数据读取使用Pandas库。

# 导入所需的库
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import lightgbm as lgb
from sklearn.metrics import mean_squared_error
from pyhht.emd import EEMD

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

# 定义函数进行数据预处理，包括EEMD分解和重构
def eemd_process(data, num_imfs):
    eemd = EEMD()
    IMFs = eemd.eemd(data)
    IMFs = np.vstack(IMFs)
    if num_imfs < IMFs.shape[0]:
        IMFs[num_imfs:, :] = 0
    imfs = np.transpose(IMFs[:num_imfs, :])
    return imfs

# 定义函数进行特征选择，使用LightGBM进行训练
def feature_selection(train_x, train_y):
    lgb_train = lgb.Dataset(train_x, train_y)
    params = {
        'boosting_type': 'gbdt',
        'objective': 'regression',
        'metric': 'rmse',
        'num_leaves': 31,
        'learning_rate': 0.05,
        'feature_fraction': 0.9
    }
    gbm = lgb.train(params, lgb_train, num_boost_round=100)
    feature_importance = gbm.feature_importance()
    feature_index = np.argsort(feature_importance)[::-1]
    return feature_index

# 定义ConvLSTM模型
class ConvLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, kernel_size, num_layers):
        super(ConvLSTM, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.kernel_size = kernel_size
        self.num_layers = num_layers
        self.padding = int((kernel_size - 1) / 2)
        self.conv = nn.Conv2d(in_channels=self.hidden_size + self.input_size,
                              out_channels=self.hidden_size * 4,
                              kernel_size=self.kernel_size,
                              padding=self.padding)
        self.hidden_cell = None
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_data):
        batch_size, seq_len, input_size, height, width = input_data.size()
        if self.hidden_cell is None:
            self.hidden_cell = (torch.zeros(batch_size, self.hidden_size, height, width).to(input_data.device),
                                torch.zeros(batch_size, self.hidden_size, height, width).to(input_data.device))
        hidden_states = []
        for seq_index in range(seq_len):
            if seq_index == 0:
                input_concat = input_data[:, seq_index, :, :, :]
                combined = torch.cat((self.hidden_cell[0], input_concat), dim=1)
                gates = self.conv(combined)
                in_gate, forget_gate, cell_gate, out_gate = gates.chunk(4, 1)
                in_gate = self.sigmoid(in_gate)
                forget_gate = self.sigmoid(forget_gate)
                cell_gate = self.relu(cell_gate