从.txt文件初始化对象向量

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import torch

# 读取.txt文件
data = pd.read_csv('data.txt', header=None)

# 将文本数据转化为向量
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(data[0])

# 将向量转化为模型可用的格式
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 将数据拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, data[1], test_size=0.2, random_state=42)

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import torch

# 读取.txt文件
data = pd.read_csv('data.txt', header=None)

# 将文本数据转化为向量
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(data[0])

# 将向量转化为模型可用的格式
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 将数据拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, data[1], test_size=0.2, random_state=42)

# 使用PyTorch中的DataLoader类将数据加载到模型中
data_loader = torch.utils.data.DataLoader(X_train, batch_size=64, shuffle=True)