朴素贝叶斯项目实战

#############################  朴素贝叶斯实战--判断肿瘤是良性的还是恶性的  #######################################

#导入数据集拆分工具
from sklearn.model_selection import train_test_split
#导入威斯康星乳腺肿瘤数据集
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()
print(‘\n\n\n‘)
print(‘代码运行结果:‘)
print(‘====================================\n‘)
#打印数据集键值
print(cancer.keys())
print(‘\n====================================‘)
print(‘\n\n\n‘)

#打印数据集中标注好的肿瘤分类
print(‘肿瘤的分类:‘,cancer[‘target_names‘])
#打印数据集中的肿瘤特征名称
print(‘\n肿瘤的特征:\n‘,cancer[‘feature_names‘])

#将数据集的数值和分类目标赋值给X和y
X,y = cancer.data,cancer.target
#使用数据集拆分工具拆分为训练集和测试集
X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=38)
print(‘\n\n\n‘)
print(‘代码运行结果:‘)
print(‘====================================\n‘)
#打印训练集和测试集的数据形态
print(‘训练集数据形态:‘,X_train.shape)
print(‘测试集数据形态:‘,X_test.shape)
print(‘\n====================================‘)
print(‘\n\n\n‘)

#导入高斯朴素贝叶斯
from sklearn.naive_bayes import GaussianNB
#使用高斯朴素贝叶斯拟合数据
gnb = GaussianNB()
gnb.fit(X_train,y_train)
print(‘\n\n\n‘)
print(‘代码运行结果:‘)
print(‘====================================\n‘)
#打印模型评分
print(‘训练集得分:{:.3f}‘.format(gnb.score(X_train,y_train)))
print(‘测试集得分:{:.3f}‘.format(gnb.score(X_test,y_test)))
print(‘\n====================================‘)
print(‘\n\n\n‘)

print(‘\n\n\n‘)
print(‘代码运行结果:‘)
print(‘====================================\n‘)
#打印模型评分
print(‘模型预测的分类:{}‘.format(gnb.predict([X[312]])))
print(‘样本的正确分类是:‘,y[312])
print(‘\n====================================‘)
print(‘\n\n\n‘)

#############################  高斯朴素贝叶斯的学习曲线  #######################################
#导入学习曲线库
from sklearn.model_selection import learning_curve
#导入随机拆分工具
from sklearn.model_selection import ShuffleSplit
#导入numpy
import numpy as np
#导入高斯朴素贝叶斯
from sklearn.naive_bayes import GaussianNB
#导入画图工具
import matplotlib.pyplot as plt
#定义一个函数绘制学习曲线
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)

    #设定横轴标签
    plt.xlabel("Training examples")
    #设定纵轴标签
    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)
    test_scores_mean = np.mean(test_scores,axis=1)
    plt.grid()
    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-valldation score")
    plt.legend(loc="lower right")
    return plt
#设定图题
title = "Learning Curves (Naive Bayes)"
#设定拆分数量
cv = ShuffleSplit(n_splits=100,test_size=0.2,random_state=0)
#设定模型为高斯朴素贝叶斯
estimator = GaussianNB()
#调用我们定义好的函数
plot_learning_curve(estimator,title,X,y,ylim=(0.9,1.01),cv=cv,n_jobs=4)
#显示图片
plt.show()