for i in range(1,len(training_set)): x_train.append(training_set[i-1:i,0]) y_train.append(training_set[i,0])
时间: 2024-06-02 22:11:06 浏览: 17
这段代码是用来将训练集数据进行切片,并将切片后的数据添加到相应的训练数据列表中。具体来说,这段代码使用了 Python 中的 for 循环和 range 函数,对训练集数据进行遍历。在每次循环中,通过索引 i 将训练集中第 i-1 行和第 i 行数据进行切片,得到一个形状为 (1, 1) 的 NumPy 数组。然后将这个数组添加到 x_train 列表中。同时,将训练集中第 i 行数据的值添加到 y_train 列表中。最终,x_train 和 y_train 列表中分别包含了训练集数据的输入和输出。
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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))
```
帮我为下面的代码加上注释:class SimpleDeepForest: def __init__(self, n_layers): self.n_layers = n_layers self.forest_layers = [] def fit(self, X, y): X_train = X for _ in range(self.n_layers): clf = RandomForestClassifier() clf.fit(X_train, y) self.forest_layers.append(clf) X_train = np.concatenate((X_train, clf.predict_proba(X_train)), axis=1) return self def predict(self, X): X_test = X for i in range(self.n_layers): X_test = np.concatenate((X_test, self.forest_layers[i].predict_proba(X_test)), axis=1) return self.forest_layers[-1].predict(X_test[:, :-2]) # 1. 提取序列特征(如:GC-content、序列长度等) def extract_features(fasta_file): features = [] for record in SeqIO.parse(fasta_file, "fasta"): seq = record.seq gc_content = (seq.count("G") + seq.count("C")) / len(seq) seq_len = len(seq) features.append([gc_content, seq_len]) return np.array(features) # 2. 读取相互作用数据并创建数据集 def create_dataset(rna_features, protein_features, label_file): labels = pd.read_csv(label_file, index_col=0) X = [] y = [] for i in range(labels.shape[0]): for j in range(labels.shape[1]): X.append(np.concatenate([rna_features[i], protein_features[j]])) y.append(labels.iloc[i, j]) return np.array(X), np.array(y) # 3. 调用SimpleDeepForest分类器 def optimize_deepforest(X, y): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model = SimpleDeepForest(n_layers=3) model.fit(X_train, y_train) y_pred = model.predict(X_test) print(classification_report(y_test, y_pred)) # 4. 主函数 def main(): rna_fasta = "RNA.fasta" protein_fasta = "pro.fasta" label_file = "label.csv" rna_features = extract_features(rna_fasta) protein_features = extract_features(protein_fasta) X, y = create_dataset(rna_features, protein_features, label_file) optimize_deepforest(X, y) if __name__ == "__main__": main()
# Define a class named 'SimpleDeepForest'
class SimpleDeepForest:
# Initialize the class with 'n_layers' parameter
def __init__(self, n_layers):
self.n_layers = n_layers
self.forest_layers = []
# Define a method named 'fit' to fit the dataset into the classifier
def fit(self, X, y):
X_train = X
# Use the forest classifier to fit the dataset for 'n_layers' times
for _ in range(self.n_layers):
clf = RandomForestClassifier()
clf.fit(X_train, y)
# Append the classifier to the list of forest layers
self.forest_layers.append(clf)
# Concatenate the training data with the predicted probability of the last layer
X_train = np.concatenate((X_train, clf.predict_proba(X_train)), axis=1)
# Return the classifier
return self
# Define a method named 'predict' to make predictions on the test set
def predict(self, X):
X_test = X
# Concatenate the test data with the predicted probability of each layer
for i in range(self.n_layers):
X_test = np.concatenate((X_test, self.forest_layers[i].predict_proba(X_test)), axis=1)
# Return the predictions of the last layer
return self.forest_layers[-1].predict(X_test[:, :-2])
# Define a function named 'extract_features' to extract sequence features
def extract_features(fasta_file):
features = []
# Parse the fasta file to extract sequence features
for record in SeqIO.parse(fasta_file, "fasta"):
seq = record.seq
gc_content = (seq.count("G") + seq.count("C")) / len(seq)
seq_len = len(seq)
features.append([gc_content, seq_len])
# Return the array of features
return np.array(features)
# Define a function named 'create_dataset' to create the dataset
def create_dataset(rna_features, protein_features, label_file):
labels = pd.read_csv(label_file, index_col=0)
X = []
y = []
# Create the dataset by concatenating the RNA and protein features
for i in range(labels.shape[0]):
for j in range(labels.shape[1]):
X.append(np.concatenate([rna_features[i], protein_features[j]]))
y.append(labels.iloc[i, j])
# Return the array of features and the array of labels
return np.array(X), np.array(y)
# Define a function named 'optimize_deepforest' to optimize the deep forest classifier
def optimize_deepforest(X, y):
# Split the dataset into training set and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create an instance of the SimpleDeepForest classifier with 3 layers
model = SimpleDeepForest(n_layers=3)
# Fit the training set into the classifier
model.fit(X_train, y_train)
# Make predictions on the testing set
y_pred = model.predict(X_test)
# Print the classification report
print(classification_report(y_test, y_pred))
# Define the main function to run the program
def main():
rna_fasta = "RNA.fasta"
protein_fasta = "pro.fasta"
label_file = "label.csv"
# Extract the RNA and protein features
rna_features = extract_features(rna_fasta)
protein_features = extract_features(protein_fasta)
# Create the dataset
X, y = create_dataset(rna_features, protein_features, label_file)
# Optimize the DeepForest classifier
optimize_deepforest(X, y)
# Check if the program is being run as the main program
if __name__ == "__main__":
main()