Python打卡训练营学习记录Day20

import pandas as pd    #用于数据处理和分析，可处理表格数据。
import numpy as np     #用于数值计算，提供了高效的数组操作。
import matplotlib.pyplot as plt    #用于绘制各种类型的图表
import seaborn as sns   #基于matplotlib的高级绘图库，能绘制更美观的统计图形。
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedKFold, cross_validate # 引入分层 K 折和交叉验证工具
from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report
import time
import warnings
warnings.filterwarnings("ignore")
# 设置中文字体（解决中文显示问题）
plt.rcParams['font.sans-serif'] = ['SimHei']  # Windows系统常用黑体字体
plt.rcParams['axes.unicode_minus'] = False    # 正常显示负号
data  = pd.read_csv('heart.csv')
# 提取连续值特征
continuous_features = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']
# 提取离散值特征
discrete_features = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'target']
# 使用映射字典进行转换
mapping = {
    'cp': {0: 0, 1: 1, 2: 2, 3: 3},
    'restecg': {0: 0, 1: 1, 2: 2},
    'slope': {0: 0, 1: 1, 2: 2},
    'ca': {0: 0, 1: 1, 2: 2, 3: 3, 4: 4},
    'thal': {0: 0, 1: 1, 2: 2, 3: 3}
}
for feature, mapping in mapping.items():
    data[feature] = data[feature].map(mapping)
# Purpose 独热编码
columns_to_encode = ['sex','fbs','exang']
data = pd.get_dummies(data, columns=columns_to_encode, drop_first=True)
data2 = pd.read_csv("heart.csv") # 重新读取数据，用来做列名对比
list_final = [] # 新建一个空列表，用于存放独热编码后新增的特征名
for i in data.columns:
    if i not in data2.columns:
        list_final.append(i) # 这里打印出来的就是独热编码后的特征名
for i in list_final:
    data[i] = data[i].astype(int) # 这里的i就是独热编码后的特征名
# 划分训练集和测试机
X = data.drop(['target'], axis=1)  # 特征，axis=1表示按列删除
y = data['target']  # 标签
# 按照8:2划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # 80%训练集，20%测试集


from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# 对训练集进行 SVD 分解
U_train, sigma_train, Vt_train = np.linalg.svd(X_train, full_matrices=False)
print(f"Vt_train 矩阵形状: {Vt_train.shape}")

# 选择保留的奇异值数量 k
k = 10
Vt_k = Vt_train[:k, :]  # 保留前 k 行，形状为 (k, 50)
print(f"保留 k={k} 后的 Vt_k 矩阵形状: {Vt_k.shape}")

# 降维训练集：X_train_reduced = X_train @ Vt_k.T
X_train_reduced = X_train @ Vt_k.T
print(f"降维后训练集形状: {X_train_reduced.shape}")

# 使用相同的 Vt_k 对测试集进行降维：X_test_reduced = X_test @ Vt_k.T
X_test_reduced = X_test @ Vt_k.T
print(f"降维后测试集形状: {X_test_reduced.shape}")

# 训练模型（以逻辑回归为例）
model = LogisticRegression(random_state=42)
model.fit(X_train_reduced, y_train)

# 预测并评估
y_pred = model.predict(X_test_reduced)
accuracy = accuracy_score(y_test, y_pred)
print(f"测试集准确率: {accuracy}")

# 计算训练集的近似误差（可选，仅用于评估降维效果）
X_train_approx = U_train[:, :k] @ np.diag(sigma_train[:k]) @ Vt_k
error = np.linalg.norm(X_train - X_train_approx, 'fro') / np.linalg.norm(X_train, 'fro')
print(f"训练集近似误差 (Frobenius 范数相对误差): {error}")

 

 

@浙大疏锦行