#导入pandas工具包，并且更名为pd
import pandas as pd
#调用pandas工具包的read_csv函数/模块，传入训练文件的地址参数，获得返回的数据且存至变量df_train.
df_train=pd.read_csv('../Datasets/Breast-Cancer/breast-cancer-train.csv')
#调用pandas工具包的read_csv函数、模块，传入测试的文件地址参数，获得返回的数据存至变量df_test
df_test=pd.read_csv('../Datasets/Breast-Cancer/breast-cancer-test.csv')
#选取‘Clump Thickness' 与'Cell Size' 作为特征，构建测试集中的正负分类样本。
df_test_negative = df_test.loc[df_test['Type'] == 0][['Clump Thickness', 'Cell Size']]
df_test_positive = df_test.loc[df_test['Type'] == 1][['Clump Thickness', 'Cell Size']]
#导入matplotlib工具包中的pyplot并简化为plt
import matplotlib.pyplot as plt
#绘制图1-2中的良心肿瘤样本点，标记为红色
plt.scatter(df_test_negative['Clump Thickness'], df_test_negative['Cell Size'], marker='o', s=200, c='red')
#绘制图1-2中的恶性肿瘤样本点，标记为黑色的x.
plt.scatter(df_test_positive['Clump Thickness'], df_test_positive['Cell Size'], marker='x', s=150, c='black')
#绘制x,y轴说明
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
#显示图1-2
plt.show()
#导入numpy工具包，并且重命名为np
import numpy as np
#利用numpy中的random函数随机采用直线的截距和系数
intercept=np.random.random([1])
coef=np.random.random([2])
lx=np.arange(0, 12)
ly=(-intercept-lx*coef[0]/coef[1])
#绘制一条随机直线
plt.plot(lx, ly, c='yellow')
#绘制图1-3
plt.scatter(df_test_negative['Clump Thickness'], df_test_negative['Cell Size'], marker='o', s=200, c='red')
plt.scatter(df_test_positive['Clump Thickness'], df_test_positive['Cell Size'], marker='x', s=150, c='black')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
plt.show()
#导入sklearn 中的逻辑斯蒂回归分类器
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
#使用前10条训练样本学习直线的系数和截距。
lr.fit(df_train[['Clump Thickness', 'Cell Size']][:10], df_train['Type'][:10])
print('Testing accuracy(10 training samples):', lr.score(df_test[['Clump Thickness', 'Cell Size']], df_test['Type']))
intercept=lr.intercept_
coef=lr.coef_[0,:]
#原本这个分类面应该是lx*coef[0]+ly*coef[1]+intercept=0,映射到2维平面上之后，应该是
ly=(-intercept-lx*coef[0])/coef[1]
# 绘制图1-4
plt.plot(lx,ly,c='green')
plt.scatter(df_test_negative['Clump Thickness'], df_test_negative['Cell Size'], marker='o', s=200, c='red')
plt.scatter(df_test_positive['Clump Thickness'], df_test_positive['Cell Size'], marker='x', s=200, c='black')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
plt.show()
lr=LogisticRegression()
#使用所有训练样本学习直线的系数和截距。
lr.fit(df_train[['Clump Thickness', 'Cell Size']], df_train['Type'])
print('Testing accuracy(all training samples:', lr.score(df_test[['Clump Thickness', 'Cell Size']], df_test['Type']))
intercept=lr.intercept_
coef=lr.coef_[0, :]
ly=(-intercept -lx*coef[0])/coef[1]
#绘制图1-5
plt.plot(lx,ly,c='blue')
plt.scatter(df_test_negative['Clump Thickness'], df_test_negative['Cell Size'], marker='o', s=200, c='red')
plt.scatter(df_test_positive['Clump Thickness'], df_test_positive['Cell Size'], marker='x', s=150, c='black')
plt.plot(lx, ly, c='blue')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
plt.show()
图1-5
