逻辑回归算法——乳腺癌检测

 #载入数据

    
 from sklearn.datasets import load_breast_cancer
    
  
    
 cancer=load_breast_cancer()
    
 X=cancer.data
    
 y=cancer.target
    
 print('data shape:{0};no. posttive:{1};no. negative:{2}'.format(X.shape,y[y==1].shape[0],y[y==0].shape[0]))
    
 #data shape:(569, 30);no. posttive:357;no. negative:212
    
    
    
    

 from sklearn.model_selection import train_test_split

    
 from sklearn.linear_model import LogisticRegression
    
 import numpy as np
    
  
    
 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)
    
 model=LogisticRegression()
    
 model.fit(X_train,y_train)
    
  
    
 train_score=model.score(X_train,y_train)
    
 test_score=model.score(X_test,y_test)
    
 print('train score:{0:.6f};test score:{1:.6f}'.format(train_score,test_score))
    
  
    
 #检查预测正确率
    
 y_pred=model.predict(X_test)
    
 print('matches:{0}/{1}'.format(np.equal(y_pred,y_test).shape[0],y_test.shape[0]))
    
    
    
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/ACgRG5i47hbjIslDMqtmZuWVXa03.png)

 #模型预测的自信度

    
 #找出自信度不足90%的模型预测
    
 y_pred_proba=model.predict_proba(X_test)
    
 result=y_pred_proba[y_pred_proba[:,0]>0.1]#y_pred_proba[:,0]>0.1获得布尔值，最后显示true值
    
 result[result[:,1]>0.1]
    
    
    
    

 #模型优化

    
 #用多项式特征
    
 from sklearn.linear_model import LogisticRegression
    
 from sklearn.preprocessing import PolynomialFeatures
    
 from sklearn.pipeline import Pipeline
    
 import time
    
  
    
 #约定俗成:
    
 #*args：可以理解为长度不固定的列表。
    
 #**kwarg：可以理解为长度不固定的字典
    
 def polynomial_model(degree=1,**kwarg):
    
     polynomial_features = PolynomialFeatures(degree=degree,include_bias=False)#include_bias：默认为 True 。如果为 True 的话，那么结果中就会有 0 次幂项
    
     logistic_reg=LogisticRegression(**kwarg)
    
     pipeline=Pipeline([('polynomial_features',polynomial_features),
    
                    ('logistic_reg',logistic_reg)])
    
     return pipeline
    
  
    
 model=polynomial_model(degree=2,penalty='l1',solver='liblinear')#penalty:惩罚 
    
 #决定惩罚项选择的有2个参数：dual和solver，如果要选L1范数，dual必须是False，solver必须是liblinear
    
 #L1范数作为正则项，可使参数稀疏化，即自动帮助我们选出那些对模型有关联的特征
    
  
    
 start=time.perf_counter()
    
 model.fit(X_train,y_train)
    
 poly_train_score=model.score(X_train,y_train)
    
 poly_test_score=model.score(X_test,y_test)
    
 print('elaspe:{0:.6f};poly_train_score:{1:.6f};poly_test_score:{2:.6f}'.format(time.perf_counter()-start,poly_train_score,poly_test_score))
    
    
    
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/xS8VERmDCtzrWLI7yFYhNg64J2Up.png)

 #查看有多少特征没有被丢弃，即对应的参数西塔非0

    
 coefs=model.named_steps['logistic_reg'].coef_
    
 #named_steps 流水线中的一条流水线名
    
 #coef_ 属性里保存的就是模型参数
    
 print("总特征数:{0};非0特征数:{1}".format(coefs.shape, np.count_nonzero(coefs)))
    
 #输入特征由原来的 30个增加到了 495 个，最终大多数特征都被丢弃，只保留了86 个有效特
    
    
    
    

 import numpy as np

    
 import matplotlib.pyplot as plt
    
 from sklearn.model_selection import learning_curve
    
  
    
 plt.rcParams['font.sans-serif'] = ['SimHei']#显示中文
    
  
    
 def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
    
                     n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
    
     plt.figure()
    
     plt.title(title)
    
     if ylim is not None:
    
     plt.ylim(*ylim)
    
     plt.xlabel("Training examples")
    
     plt.ylabel("Score")
    
     train_sizes, train_scores, test_scores = learning_curve(
    
     estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
    
     train_scores_mean = np.mean(train_scores, axis=1)
    
     train_scores_std = np.std(train_scores, axis=1)
    
     test_scores_mean = np.mean(test_scores, axis=1)
    
     test_scores_std = np.std(test_scores, axis=1)
    
     plt.grid()# 生成网格
    
     plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
    
                  train_scores_mean + train_scores_std, alpha=0.1,
    
                  color="r")
    
     plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
    
                  test_scores_mean + test_scores_std, alpha=0.1, color="g")
    
     plt.plot(train_sizes, train_scores_mean, 'o-', color="r",label="Training score")
    
     plt.plot(train_sizes, test_scores_mean, 'o-', color="g",label="Cross-validation score") 
    
     plt.legend(loc="best")#添加图例
    
     return plt
    
  
    
 from sklearn.model_selection import ShuffleSplit
    
 import time
    
  
    
 cv=ShuffleSplit(n_splits=10,test_size=0.2,random_state=0)
    
  
    
 #start=time.perf_counter()
    
 plot_learning_curve(polynomial_model(degree=1,penalty='l1',solver='liblinear'),"Learning Curves (degree=1 , penalty=l1)",X,y,ylim=(0.8,1.01),cv=cv)
    
 plot_learning_curve(polynomial_model(degree=2,penalty='l1',solver='liblinear'),"Learning Curves (degree=2 , penalty=l1)",X,y,ylim=(0.8,1.01),cv=cv)
    
 plot_learning_curve(polynomial_model(degree=1,penalty='l2',solver='lbfgs'),"Learning Curves (degree=1 , penalty=l2)",X,y,ylim=(0.8,1.01),cv=cv)
    
 plot_learning_curve(polynomial_model(degree=2,penalty='l2',solver='lbfgs'),"Learning Curves (degree=2 , penalty=l2)",X,y,ylim=(0.8,1.01),cv=cv)
    
    
    
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/KJ079GLQqN6syAhOcRv8oZkCljDE.png)