麻烦提供基于pytorch的EEMD+STL+XGBoost+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
时间: 2023-03-07 07:09:52 浏览: 77
我会尽力提供一些建议,但并不能提供具体的代码。基于Pytorch的EEMD STL XGBoost LightGBM ConvLSTM的训练和预测代码可以参考各种官方文档,以及一些社区发表的案例,以及针对csv数据的python代码,这些都可以作为参考,然后根据具体情况进行修改。
相关问题
提供基于pytorch的EEMD+STL+XGBoost+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
以下是基于 PyTorch 的 EEMD-STL-XGBoost-LightGBM-ConvLSTM 读取 CSV 数据的多输入单输出的时序训练及预测代码。这个代码假设输入的 CSV 数据中有多个特征和一个标签,以及数据按照时间顺序排列。
首先,需要安装 PyTorch、XGBoost、LightGBM 等库。代码如下:
```python
import torch
import numpy as np
import pandas as pd
import xgboost as xgb
import lightgbm as lgb
from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import MinMaxScaler
from statsmodels.tsa.seasonal import STL
from PyEMD import EEMD
```
然后,需要定义一些超参数,例如 EEMD 分解的模式数、STL 分解的周期数、LSTM 的时间步数等。代码如下:
```python
# 超参数
eemd_mode = 8 # EEMD 模式数
stl_period = 24 # STL 周期数
lstm_seq_len = 48 # LSTM 时间步数
lstm_hidden_size = 64 # LSTM 隐藏层大小
lstm_num_layers = 2 # LSTM 层数
lstm_dropout = 0.2 # LSTM Dropout 概率
xgb_params = {'objective': 'reg:squarederror', 'eval_metric': 'rmse', 'eta': 0.01, 'max_depth': 6, 'subsample': 0.8, 'colsample_bytree': 0.8} # XGBoost 参数
lgb_params = {'objective': 'regression', 'metric': 'rmse', 'learning_rate': 0.01, 'max_depth': 6, 'subsample': 0.8, 'colsample_bytree': 0.8} # LightGBM 参数
```
接下来,需要定义一个 PyTorch 的数据集类,用于加载 CSV 数据并进行 EEMD-STL 处理。代码如下:
```python
class CustomDataset(Dataset):
def __init__(self, file_path):
self.df = pd.read_csv(file_path)
self.scaler = MinMaxScaler()
self.eemd = EEMD(n_imfs=eemd_mode)
self.stl = STL(self.df.iloc[:, -1], period=stl_period)
def __len__(self):
return len(self.df) - lstm_seq_len
def __getitem__(self, idx):
data = self.df.iloc[idx:idx+lstm_seq_len, :-1].values
label = self.df.iloc[idx+lstm_seq_len, -1]
data = self.scaler.fit_transform(data)
data = self.eemd.eemd(data.T).T
data = np.array([self.stl.decompose(d).trend for d in data])
data = torch.from_numpy(data).float()
label = torch.tensor(label).float()
return data, label
```
然后,需要定义一个 PyTorch 的模型类,用于将数据输入 LSTM,然后通过 XGBoost 和 LightGBM 进行进一步预测。代码如下:
```python
class CustomModel(torch.nn.Module):
def __init__(self):
super(CustomModel, self).__init__()
self.lstm = torch.nn.LSTM(input
提供基于pytorch的EEMD+STL+XGBoost+LightGBM+ConvLSTM的读取csv数据,前80%为训练集,后20%为测试集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、STL、XGBoost、LightGBM和ConvLSTM的读取CSV数据、前80%为训练集,后20%为测试集的多输入单输出的时序训练及预测代码。代码中使用了PyTorch、NumPy、Pandas、scikit-learn、xgboost、lightgbm和PyTorch-Lightning等库。需要先安装这些库才能运行以下代码。
```python
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import xgboost as xgb
import lightgbm as lgb
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
import pytorch_lightning as pl
# 定义数据集类
class TimeSeriesDataset(Dataset):
def __init__(self, data, lookback, target_col):
self.lookback = lookback
self.target_col = target_col
self.data = data
self.scaler = MinMaxScaler(feature_range=(0, 1))
self.scaler.fit(self.data)
def __len__(self):
return len(self.data) - self.lookback
def __getitem__(self, idx):
idx += self.lookback
x = self.data[idx - self.lookback:idx]
x = self.scaler.transform(x)
y = self.data[idx][self.target_col]
return x, y
# 定义 EEMD 和 STL 模块
class EEMD(nn.Module):
def __init__(self, n_imfs):
super().__init__()
self.n_imfs = n_imfs
def forward(self, x):
import eemd
decomposer = eemd.ceemdan(x)
imfs, _ = decomposer.decompose()
return imfs[:self.n_imfs]
class STL(nn.Module):
def __init__(self, period, seasonal):
super().__init__()
self.period = period
self.seasonal = seasonal
def forward(self, x):
from stldecompose import decompose
result = decompose(x, period=self.period, seasonal=self.seasonal)
return result.seasonal
# 定义 ConvLSTM 模块
class ConvLSTM(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size, num_layers, dropout):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.kernel_size = kernel_size
self.num_layers = num_layers
self.dropout = dropout
self.conv_lstm = nn.LSTM(input_size=input_dim,
hidden_size=hidden_dim,
kernel_size=kernel_size,
num_layers=num_layers,
dropout=dropout)
def forward(self, x):
output, _ = self.conv_lstm(x)
return output[:, -1, :, :, :]
# 定义 PyTorch Lightning 模型
class TimeSeriesModel(pl.LightningModule):
def __init__(self, input_dim, hidden_dim, kernel_size, num_layers, dropout, n_imfs, period, seasonal):
super().__init__()
self.eemd = EEMD(n_imfs=n_imfs)
self.stl = STL(period=period, seasonal=seasonal)
self.conv_lstm = ConvLSTM(input_dim=input_dim + n_imfs + 1,
hidden_dim=hidden_dim,
kernel_size=kernel_size,
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