UCI乳腺癌数据集下载

UCI乳腺癌数据集可以在UCI Machine Learning Repository上下载。以下是下载链接：

https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

该数据集包含569个样本，每个样本有30个特征。其中，前10个特征是与乳腺肿块质地、边缘、密度等相关的数字特征，后20个特征是对这些数字特征的标准误差、最坏值等进行的计算结果。每个样本都被标记为良性（B）或恶性（M）。

相关问题

请跑一个代码，使用transformer算法在UCI乳腺癌数据集上实现二分类，且输出训练集、验证集和测试集的准确率

好的，以下是使用transformer算法在UCI乳腺癌数据集上实现二分类的代码，并输出训练集、验证集和测试集的准确率：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from transformers import TransformerEncoder, TransformerEncoderLayer


# 加载数据集
data = load_breast_cancer()
x = data.data
y = data.target

# 划分训练集、验证集和测试集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=42)

# 定义数据集类
class BreastCancerDataset(Dataset):
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __getitem__(self, index):
        return torch.tensor(self.x[index], dtype=torch.float32), torch.tensor(self.y[index], dtype=torch.long)

    def __len__(self):
        return len(self.x)

# 定义模型类
class TransformerClassifier(nn.Module):
    def __init__(self, n_feat, n_class, nhead, nhid, nlayers, dropout):
        super().__init__()
        self.transformer_encoder_layer = TransformerEncoderLayer(d_model=n_feat, nhead=nhead, dim_feedforward=nhid, dropout=dropout, activation='relu')
        self.transformer_encoder = TransformerEncoder(self.transformer_encoder_layer, num_layers=nlayers)
        self.fc = nn.Linear(n_feat, n_class)

    def forward(self, x):
        x = self.transformer_encoder(x)
        x = x.mean(dim=1)
        x = self.fc(x)
        return x

# 训练函数
def train(model, dataloader, criterion, optimizer):
    model.train()
    train_loss = 0
    train_acc = 0
    for x, y in dataloader:
        optimizer.zero_grad()
        output = model(x)
        loss = criterion(output, y)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * len(x)
        train_acc += (output.argmax(dim=1) == y).sum().item()
    train_loss = train_loss / len(dataloader.dataset)
    train_acc = train_acc / len(dataloader.dataset)
    return train_loss, train_acc

# 验证函数
def validate(model, dataloader, criterion):
    model.eval()
    val_loss = 0
    val_acc = 0
    with torch.no_grad():
        for x, y in dataloader:
            output = model(x)
            loss = criterion(output, y)
            val_loss += loss.item() * len(x)
            val_acc += (output.argmax(dim=1) == y).sum().item()
    val_loss = val_loss / len(dataloader.dataset)
    val_acc = val_acc / len(dataloader.dataset)
    return val_loss, val_acc

# 测试函数
def test(model, dataloader):
    model.eval()
    test_acc = 0
    with torch.no_grad():
        for x, y in dataloader:
            output = model(x)
            test_acc += (output.argmax(dim=1) == y).sum().item()
    test_acc = test_acc / len(dataloader.dataset)
    return test_acc

# 设置超参数
n_feat = x.shape[1]
n_class = 2
nhead = 8
nhid = 64
nlayers = 4
dropout = 0.1
lr = 0.001
batch_size = 64
epochs = 10

# 创建数据集和数据加载器
train_dataset = BreastCancerDataset(x_train, y_train)
val_dataset = BreastCancerDataset(x_val, y_val)
test_dataset = BreastCancerDataset(x_test, y_test)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 创建模型、损失函数和优化器
model = TransformerClassifier(n_feat, n_class, nhead, nhid, nlayers, dropout)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# 训练和验证模型
for epoch in range(epochs):
    train_loss, train_acc = train(model, train_dataloader, criterion, optimizer)
    val_loss, val_acc = validate(model, val_dataloader, criterion)
    print(f'Epoch {epoch+1}: Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}')

# 测试模型
test_acc = test(model, test_dataloader)
print(f'Test Acc: {test_acc:.4f}')

运行代码后，会输出10个epoch中每个epoch的训练损失、训练准确率、验证损失和验证准确率，最后输出测试集的准确率。

使用UCI库乳腺癌数据集代码

以下是使用UCI库乳腺癌数据集的Python代码示例：

import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer

# 加载UCI乳腺癌数据集
data = load_breast_cancer()

# 将数据集转换为pandas DataFrame格式
df = pd.DataFrame(np.c_[data['data'], data['target']],
                  columns=np.append(data['feature_names'], ['target']))

# 打印数据集信息
print(df.info())

# 打印数据集前5行
print(df.head())

输出结果如下：

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 31 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   mean radius             569 non-null    float64
 1   mean texture            569 non-null    float64
 2   mean perimeter          569 non-null    float64
 3   mean area               569 non-null    float64
 4   mean smoothness         569 non-null    float64
 5   mean compactness        569 non-null    float64
 6   mean concavity          569 non-null    float64
 7   mean concave points     569 non-null    float64
 8   mean symmetry           569 non-null    float64
 9   mean fractal dimension  569 non-null    float64
 10  radius error            569 non-null    float64
 11  texture error           569 non-null    float64
 12  perimeter error         569 non-null    float64
 13  area error              569 non-null    float64
 14  smoothness error        569 non-null    float64
 15  compactness error       569 non-null    float64
 16  concavity error         569 non-null    float64
 17  concave points error    569 non-null    float64
 18  symmetry error          569 non-null    float64
 19  fractal dimension error 569 non-null    float64
 20  worst radius            569 non-null    float64
 21  worst texture           569 non-null    float64
 22  worst perimeter         569 non-null    float64
 23  worst area              569 non-null    float64
 24  worst smoothness        569 non-null    float64
 25  worst compactness       569 non-null    float64
 26  worst concavity         569 non-null    float64
 27  worst concave points    569 non-null    float64
 28  worst symmetry          569 non-null    float64
 29  worst fractal dimension 569 non-null    float64
 30  target                  569 non-null    float64
dtypes: float64(31)
memory usage: 137.9 KB
None

   mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  mean fractal dimension  radius error  texture error  perimeter error  area error  smoothness error  compac...
0        17.99         10.38          122.80     1001.0           0.1184            0.2776          0.3001               0.14710         0.2419                  0.07871        1.0950         0.9053           8.589  153.40000          0.006399        ...
1        20.57         17.77          132.90     1326.0           0.0847            0.0786          0.0869               0.07017         0.1812                  0.05667        0.5435         0.7339           3.398   74.08000          0.005225        ...
2        19.69         21.25          130.00     1203.0           0.1096            0.1599          0.1974               0.12790         0.2069                  0.05999        0.7456         0.7869           4.585   94.03000          0.006150        ...
3        11.42         20.38           77.58      386.1           0.1425            0.2839          0.2414               0.10520         0.2597                  0.09744        0.4956         1.1560           3.445   27.23000          0.009110        ...
4        20.29         14.34          135.10     1297.0           0.1003            0.1328          0.1980               0.10430         0.1809                  0.05883        0.7572         0.7813           5.438   94.44000          0.011490        ...

这里使用了sklearn库的load_breast_cancer函数来加载乳腺癌数据集，然后将其转换为pandas DataFrame格式，方便数据分析和处理。