安远县城乡规划建设局网站,七牛云 wordpress,华强北做网站,企业官网设计1.项目说明
**选用Close和Low两个特征#xff0c;使用窗口time_steps窗口的2个特征#xff0c;然后预测Close这一个特征数据未来一天的数据
当batch_firstTrue,则LSTM的inputs(batch_size,time_steps,input_size)
batch_size len(data)-time_steps time_steps 滑动窗口使用窗口time_steps窗口的2个特征然后预测Close这一个特征数据未来一天的数据
当batch_firstTrue,则LSTM的inputs(batch_size,time_steps,input_size)
batch_size len(data)-time_steps time_steps 滑动窗口本项目中值为lookback input_size 2【因为选取了Close和Low两个特征】**
2.数据集
参考https://blog.csdn.net/qq_38633279/article/details/134245512?spm1001.2014.3001.5501中的数据集3.数据预处理
3.1 读取数据
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
import torch
import torch.nn as nn
import seaborn as sns
import math, time
from sklearn.metrics import mean_squared_errorfilepath ./data/rlData.csv
data pd.read_csv(filepath)
data data.sort_values(Date)
data.head()
data.shapesns.set_style(darkgrid)
plt.figure(figsize (15,9))
plt.plot(data[[Close]])
plt.xticks(range(0,data.shape[0],20), data[Date].loc[::20], rotation45)
plt.title(****** Stock Price,fontsize18, fontweightbold)
plt.xlabel(Date,fontsize18)
plt.ylabel(Close Price (USD),fontsize18)
plt.show()3.2 选取Close和Low两个特征
price data[[Close, Low]]3.3 数据归一化
scaler MinMaxScaler(feature_range(-1, 1))
price[Close] scaler.fit_transform(price[Close].values.reshape(-1,1))
price[Low] scaler.fit_transform(price[Low].values.reshape(-1,1))3.4 数据集的制造[batch_size,time_steps,input_size]
本次选取2个维度特征作为输出因此input_size 2 x_train.shape [batch_size,time_steps,input_size] y_train.shape [batch_size,1]
1. 输入选取的是Close和Low列作为多维度的输入所以选择的是data数据中的第一列和第二列作为x_train【因此input_size2】 2. 输出是选取的Close列作为预测所以选取data数据的第一列作为y_train【即Close列作为y_train】。
#2.数据集的制作
def split_data(stock, lookback):data_raw stock.to_numpy() data [] for index in range(len(data_raw) - lookback): data.append(data_raw[index: index lookback])data np.array(data);test_set_size int(np.round(0.2 * data.shape[0]))train_set_size data.shape[0] - (test_set_size)x_train data[:train_set_size,:-1,:] #x_train.shape (198, 4, 2)y_train data[:train_set_size,-1,0:1] #y_train.shape (198, 1)x_test data[train_set_size:,:-1,:] #x_test.shape (49, 4, 2)y_test data[train_set_size:,-1,0:1] #y_test.shape (49, 1)return [torch.Tensor(x_train), torch.Tensor(y_train), torch.Tensor(x_test),torch.Tensor(y_test)]lookback 5
x_train, y_train, x_test, y_test split_data(price, lookback)
print(x_train.shape ,x_train.shape)
print(y_train.shape ,y_train.shape)
print(x_test.shape ,x_test.shape)
print(y_test.shape ,y_test.shape)4.LSTM算法
这里的LSTM算法和单维单步预测中的LSTM预测算法一模一样。只不过我们在制作数据集的时候对于LSTM模型中输入不一样了。
class LSTM(nn.Module):def __init__(self, input_dim, hidden_dim, num_layers, output_dim):super(LSTM, self).__init__()self.hidden_dim hidden_dimself.num_layers num_layersself.lstm nn.LSTM(input_dim, hidden_dim, num_layers, batch_firstTrue)self.fc nn.Linear(hidden_dim, output_dim)def forward(self, x):h0 torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()c0 torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()out, (hn, cn) self.lstm(x, (h0.detach(), c0.detach()))out self.fc(out[:, -1, :]) 5.预训练
input_dim 2
hidden_dim 32
num_layers 2
output_dim 1
num_epochs 100model LSTM(input_diminput_dim, hidden_dimhidden_dim, output_dimoutput_dim, num_layersnum_layers)
criterion torch.nn.MSELoss()
optimiser torch.optim.Adam(model.parameters(), lr0.01)hist np.zeros(num_epochs)
lstm []for t in range(num_epochs):y_train_pred model(x_train)loss criterion(y_train_pred, y_train)hist[t] loss.item()# print(Epoch , t, MSE: , loss.item())optimiser.zero_grad()loss.backward()optimiser.step()6.绘制预测值和真实值拟合图形以及loss图形
predict pd.DataFrame(scaler.inverse_transform(y_train_pred.detach().numpy()))
original pd.DataFrame(scaler.inverse_transform(y_train.detach().numpy()))sns.set_style(darkgrid) fig plt.figure()
fig.subplots_adjust(hspace0.2, wspace0.2)plt.subplot(1, 2, 1)
ax sns.lineplot(x original.index, y original[0], labelData, colorroyalblue)
ax sns.lineplot(x predict.index, y predict[0], labelTraining Prediction (LSTM), colortomato)
ax.set_title(Stock price, size 14, fontweightbold)
ax.set_xlabel(Days, size 14)
ax.set_ylabel(Cost (USD), size 14)
ax.set_xticklabels(, size10)plt.subplot(1, 2, 2)
ax sns.lineplot(datahist, colorroyalblue)
ax.set_xlabel(Epoch, size 14)
ax.set_ylabel(Loss, size 14)
ax.set_title(Training Loss, size 14, fontweightbold)
fig.set_figheight(6)
fig.set_figwidth(16)# make predictions
y_test_pred model(x_test)# invert predictions
y_train_pred scaler.inverse_transform(y_train_pred.detach().numpy())
y_train scaler.inverse_transform(y_train.detach().numpy())
y_test_pred scaler.inverse_transform(y_test_pred.detach().numpy())
y_test scaler.inverse_transform(y_test.detach().numpy())# calculate root mean squared error
trainScore math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))
print(Train Score: %.2f RMSE % (trainScore))
testScore math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))
print(Test Score: %.2f RMSE % (testScore))
lstm.append(trainScore)
lstm.append(testScore)
lstm.append(training_time)
完整代码
问题描述
选用Close和Low两个特征使用窗口time_steps窗口的2个特征然后预测Close这一个特征数据未来一天的数据
当batch_firstTrue,则LSTM的inputs(batch_size,time_steps,input_size)
batch_size len(data)-time_steps
time_steps 滑动窗口本项目中值为lookback
input_size 2【因为选取了Close和Low两个特征】
#%%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
import torch
import torch.nn as nn
import seaborn as sns
import math, time
from sklearn.metrics import mean_squared_errorfilepath ./data/rlData.csv
data pd.read_csv(filepath)
data data.sort_values(Date)
data.head()
data.shapesns.set_style(darkgrid)
plt.figure(figsize (15,9))
plt.plot(data[[Close]])
plt.xticks(range(0,data.shape[0],20), data[Date].loc[::20], rotation45)
plt.title(****** Stock Price,fontsize18, fontweightbold)
plt.xlabel(Date,fontsize18)
plt.ylabel(Close Price (USD),fontsize18)
plt.show()#1.选取特征工程2个
price data[[Close, Low]]scaler MinMaxScaler(feature_range(-1, 1))
price[Close] scaler.fit_transform(price[Close].values.reshape(-1,1))
price[Low] scaler.fit_transform(price[Low].values.reshape(-1,1))#2.数据集的制作
def split_data(stock, lookback):data_raw stock.to_numpy() data [] for index in range(len(data_raw) - lookback): data.append(data_raw[index: index lookback])data np.array(data);test_set_size int(np.round(0.2 * data.shape[0]))train_set_size data.shape[0] - (test_set_size)x_train data[:train_set_size,:-1,:] #x_train.shape (198, 4, 2)y_train data[:train_set_size,-1,0:1] #y_train.shape (198, 1)x_test data[train_set_size:,:-1,:] #x_test.shape (49, 4, 2)y_test data[train_set_size:,-1,0:1] #y_test.shape (49, 1)return [torch.Tensor(x_train), torch.Tensor(y_train), torch.Tensor(x_test),torch.Tensor(y_test)]lookback 5
x_train, y_train, x_test, y_test split_data(price, lookback)
print(x_train.shape ,x_train.shape)
print(y_train.shape ,y_train.shape)
print(x_test.shape ,x_test.shape)
print(y_test.shape ,y_test.shape)class LSTM(nn.Module):def __init__(self, input_dim, hidden_dim, num_layers, output_dim):super(LSTM, self).__init__()self.hidden_dim hidden_dimself.num_layers num_layersself.lstm nn.LSTM(input_dim, hidden_dim, num_layers, batch_firstTrue)self.fc nn.Linear(hidden_dim, output_dim)def forward(self, x):h0 torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()c0 torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()out, (hn, cn) self.lstm(x, (h0.detach(), c0.detach()))out self.fc(out[:, -1, :]) return outinput_dim 2
hidden_dim 32
num_layers 2
output_dim 1
num_epochs 100model LSTM(input_diminput_dim, hidden_dimhidden_dim, output_dimoutput_dim, num_layersnum_layers)
criterion torch.nn.MSELoss()
optimiser torch.optim.Adam(model.parameters(), lr0.01)hist np.zeros(num_epochs)
lstm []for t in range(num_epochs):y_train_pred model(x_train)loss criterion(y_train_pred, y_train)hist[t] loss.item()# print(Epoch , t, MSE: , loss.item())optimiser.zero_grad()loss.backward()optimiser.step()predict pd.DataFrame(scaler.inverse_transform(y_train_pred.detach().numpy()))
original pd.DataFrame(scaler.inverse_transform(y_train.detach().numpy()))sns.set_style(darkgrid) fig plt.figure()
fig.subplots_adjust(hspace0.2, wspace0.2)plt.subplot(1, 2, 1)
ax sns.lineplot(x original.index, y original[0], labelData, colorroyalblue)
ax sns.lineplot(x predict.index, y predict[0], labelTraining Prediction (LSTM), colortomato)
ax.set_title(Stock price, size 14, fontweightbold)
ax.set_xlabel(Days, size 14)
ax.set_ylabel(Cost (USD), size 14)
ax.set_xticklabels(, size10)plt.subplot(1, 2, 2)
ax sns.lineplot(datahist, colorroyalblue)
ax.set_xlabel(Epoch, size 14)
ax.set_ylabel(Loss, size 14)
ax.set_title(Training Loss, size 14, fontweightbold)
fig.set_figheight(6)
fig.set_figwidth(16)# make predictions
y_test_pred model(x_test)# invert predictions
y_train_pred scaler.inverse_transform(y_train_pred.detach().numpy())
y_train scaler.inverse_transform(y_train.detach().numpy())
y_test_pred scaler.inverse_transform(y_test_pred.detach().numpy())
y_test scaler.inverse_transform(y_test.detach().numpy())# calculate root mean squared error
trainScore math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))
print(Train Score: %.2f RMSE % (trainScore))
testScore math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))
print(Test Score: %.2f RMSE % (testScore))
lstm.append(trainScore)
lstm.append(testScore)
lstm.append(training_time)
参考https://gitee.com/qiangchen_sh/stock-prediction/blob/master/%E4%BB%A3%E7%A0%81/LSTM%E4%BB%8E%E7%90%86%E8%AE%BA%E5%9F%BA%E7%A1%80%E5%88%B0%E4%BB%A3%E7%A0%81%E5%AE%9E%E6%88%98%204%20%E5%A4%9A%E7%BB%B4%E7%89%B9%E5%BE%81%E8%82%A1%E7%A5%A8%E4%BB%B7%E6%A0%BC%E9%A2%84%E6%B5%8B_Pytorch.ipynb
