y_0 = np.array(data.iloc[set_k + 1:, 7]) y_0 = y_0.reshape(1, -1) # 二值化:大于0的数映射为1(涨),小于0的数为0(跌), transformer = Binarizer(threshold=0).fit_transform(y_0) y_0 = transformer.ravel()
时间: 2024-03-07 19:51:19 浏览: 17
这段代码用于处理目标变量y,将其转化为二分类问题。具体来说,代码首先使用iloc函数获取原始数据data中从第set_k+2行到最后一行的收盘价数据,将其存储在名为y_0的numpy数组中。然后,代码使用reshape函数将y_0的形状从(样本数量,)变为(1, 样本数量),以便后续处理。接着,代码使用Binarizer函数将y_0中大于0的数映射为1,小于等于0的数映射为0,以将问题转化为二分类问题。最后,代码使用ravel函数将y_0的形状从(1, 样本数量)变为(样本数量,),以便后续模型训练。这样处理后,y_0中的每个元素表示当天股票价格的涨跌情况,1表示涨,0表示跌。
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下面的这段python代码,哪里有错误,修改一下:import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from torch.autograd import Variable from sklearn.preprocessing import MinMaxScaler training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - seq_length): _x = data[i:(i + seq_length)] _y = data[i + seq_length] x.append(_x) y.append(_y) return np.array(x), np.array(y) sc = MinMaxScaler() training_data = sc.fit_transform(training_set) seq_length = 1 x, y = sliding_windows(training_data, seq_length) train_size = int(len(y) * 0.8) test_size = len(y) - train_size dataX = Variable(torch.Tensor(np.array(x))) dataY = Variable(torch.Tensor(np.array(y))) trainX = Variable(torch.Tensor(np.array(x[1:train_size]))) trainY = Variable(torch.Tensor(np.array(y[1:train_size]))) testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) class LSTM(nn.Module): def __init__(self, num_classes, input_size, hidden_size, num_layers): super(LSTM, self).__init__() self.num_classes = num_classes self.num_layers = num_layers self.input_size = input_size self.hidden_size = hidden_size self.seq_length = seq_length self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): h_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) # Propagate input through LSTM ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out num_epochs = 2000 learning_rate = 0.001 input_size = 1 hidden_size = 2 num_layers = 1 num_classes = 1 lstm = LSTM(num_classes, input_size, hidden_size, num_layers) criterion = torch.nn.MSELoss() # mean-squared error for regression optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate) runn = 10 Y_predict = np.zeros((runn, len(dataY))) # Train the model for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range(num_epochs): outputs = lstm(trainX) optimizer.zero_grad() # obtain the loss function loss = criterion(outputs, trainY) loss.backward() optimizer.step() if epoch % 100 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) lstm.eval() train_predict = lstm(dataX) data_predict = train_predict.data.numpy() dataY_plot = dataY.data.numpy() data_predict = sc.inverse_transform(data_predict) dataY_plot = sc.inverse_transform(dataY_plot) Y_predict[i,:] = np.transpose(np.array(data_predict)) Y_Predict = np.mean(np.array(Y_predict)) Y_Predict_T = np.transpose(np.array(Y_Predict))
在这段代码中,可能存在以下错误:
1. 缺少必要的库或模块。
2. training_set 的文件路径是否正确。
3. training_set 的数据处理是否正确。
4. LSTM 模型的定义是否正确。
5. 训练和预测的代码是否正确。
下面是修改后的代码:
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler
# 读取数据
training_set = pd.read_csv('CX2-36_1971.csv')
training_set = training_set.iloc[:, 1:2].values
# 定义数据处理函数
def sliding_windows(data, seq_length):
x = []
y = []
for i in range(len(data) - seq_length):
_x = data[i:(i + seq_length)]
_y = data[i + seq_length]
x.append(_x)
y.append(_y)
return np.array(x), np.array(y)
# 对数据进行归一化处理
sc = MinMaxScaler()
training_data = sc.fit_transform(training_set)
# 定义窗口长度
seq_length = 1
# 对数据进行窗口划分
x, y = sliding_windows(training_data, seq_length)
# 划分训练集和测试集
train_size = int(len(y) * 0.8)
test_size = len(y) - train_size
dataX = Variable(torch.Tensor(np.array(x)))
dataY = Variable(torch.Tensor(np.array(y)))
trainX = Variable(torch.Tensor(np.array(x[1:train_size])))
trainY = Variable(torch.Tensor(np.array(y[1:train_size])))
testX = Variable(torch.Tensor(np.array(x[train_size:len(x)])))
testY = Variable(torch.Tensor(np.array(y[train_size:len(y)])))
# 定义 LSTM 模型
class LSTM(nn.Module):
def __init__(self, num_classes, input_size, hidden_size, num_layers):
super(LSTM, self).__init__()
self.num_classes = num_classes
self.num_layers = num_layers
self.input_size = input_size
self.hidden_size = hidden_size
self.seq_length = seq_length
self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size,
num_layers=num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, x):
h_0 = Variable(torch.zeros(
self.num_layers, x.size(0), self.hidden_size))
c_0 = Variable(torch.zeros(
self.num_layers, x.size(0), self.hidden_size))
# Propagate input through LSTM
ula, (h_out, _) = self.lstm(x, (h_0, c_0))
h_out = h_out.view(-1, self.hidden_size)
out = self.fc(h_out)
return out
# 定义训练参数
num_epochs = 2000
learning_rate = 0.001
input_size = 1
hidden_size = 2
num_layers = 1
num_classes = 1
# 实例化 LSTM 模型
lstm = LSTM(num_classes, input_size, hidden_size, num_layers)
# 定义损失函数和优化器
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate)
# 训练模型
runn = 10
Y_predict = np.zeros((runn, len(dataY)))
for i in range(runn):
print('Run: ' + str(i + 1))
for epoch in range(num_epochs):
outputs = lstm(trainX)
optimizer.zero_grad()
loss = criterion(outputs, trainY)
loss.backward()
optimizer.step()
if epoch % 100 == 0:
print("Epoch: %d, loss: %1.5f" % (epoch, loss.item()))
lstm.eval()
train_predict = lstm(dataX)
data_predict = train_predict.data.numpy()
dataY_plot = dataY.data.numpy()
# 对结果进行反归一化
data_predict = sc.inverse_transform(data_predict)
dataY_plot = sc.inverse_transform(dataY_plot)
Y_predict[i,:] = np.transpose(np.array(data_predict))
Y_Predict = np.mean(Y_predict, axis=0)
Y_Predict_T = np.transpose(np.array(Y_Predict))
```
def data_processing(data): # 日期缺失,补充 data.fillna(method='ffill', inplace=True) date_history = pd.DataFrame(data.iloc[:, 0]) data_history = pd.DataFrame(data.iloc[:, 1]) date_history = np.array(date_history) data_history = [x for item in np.array(data_history).tolist() for x in item] # 缺失值处理 history_time_list = [] for date in date_history: date_obj = datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') #将字符串转为 datetime 对象 history_time_list.append(date_obj) start_time = history_time_list[0] # 起始时间 end_time = history_time_list[-1] # 结束时间 delta = datetime.timedelta(minutes=15) #时间间隔为15分钟 time_new_list = [] current_time = start_time while current_time <= end_time: time_new_list.append(current_time) current_time += delta # 缺失位置记录 code_list = [] for i in range(len(time_new_list)): code_list = code_list history_time_list = history_time_list while (time_new_list[i] - history_time_list[i]) != datetime.timedelta(minutes=0): history_time_list.insert(i, time_new_list[i]) code_list.append(i) for i in code_list: data_history.insert(i, data_history[i - 1]) # 输出补充好之后的数据 data = pd.DataFrame({'date': time_new_list, 'load': data_history}) return data 代码优化
1. 可以将 `date_history` 和 `data_history` 的创建合并成一行:
```
date_history, data_history = np.array(data.iloc[:, 0]), [x for item in np.array(data.iloc[:, 1]).tolist() for x in item]
```
2. 可以在遍历 `date_history` 时,直接将字符串转为 datetime 对象,并添加到 `history_time_list` 中:
```
history_time_list = [datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') for date in date_history]
```
3. 在记录缺失位置时,可以用 `zip()` 函数将 `time_new_list` 和 `history_time_list` 同时遍历,这样会更加简洁:
```
code_list = []
for new_time, history_time in zip(time_new_list, history_time_list):
while (new_time - history_time) != datetime.timedelta(minutes=0):
history_time_list.insert(i, new_time)
code_list.append(i)
```
4. 可以使用 `pandas` 的 `interpolate()` 方法来进行缺失值插值,这样可以省去很多代码:
```
data = data.set_index('date').resample('15T').interpolate().reset_index()
```
综上所述,优化后的代码如下:
```
def data_processing(data):
data.fillna(method='ffill', inplace=True)
date_history, data_history = np.array(data.iloc[:, 0]), [x for item in np.array(data.iloc[:, 1]).tolist() for x in item]
history_time_list = [datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') for date in date_history]
start_time, end_time, delta = history_time_list[0], history_time_list[-1], datetime.timedelta(minutes=15)
time_new_list = [start_time + i * delta for i in range(int((end_time - start_time) / delta.total_seconds() / 60) + 1)]
data = pd.DataFrame({'date': time_new_list, 'load': data_history})
data = data.set_index('date').resample('15T').interpolate().reset_index()
return data
```