标签：body ror pytho ros head regress targe lib val
在开始说之前一个很重要的Tip：电脑至少要求是64位的，这是我的痛。
断断续续花了个把月的时间把这本书过了一遍。这是一本非常适合基于python入门的机器学习入门的书籍，全书通俗易懂且有代码提供。书中源代码连接为Ipython环境。主页君使用的是pycharm，python2.7，具体安转过程书本写的很详细。码完书中代码，有一点点点小不符（或许可能是因为平台不一样），百度基本可以解决问题（有问题也可以留言探讨）。贴一点代码，以示学习：
1_4_7.py:
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#coding=utf-8# Filename : 良性、恶性乳腺癌肿瘤预测 完整代码样例(线性分类器)#导入pandas工具包import pandas as pd#调用pandas工具包的read_csv函数模块，传入训练文件地址参数，获得返回的数据并且存入变量df_traindf_train=pd.read_csv(‘breast-cancer-train.csv‘)df_test=pd.read_csv(‘breast-cancer-test.csv‘)#选取clumpthickness与cellsize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‘]]import matplotlib.pyplot as plt#绘制良性肿瘤样本点标记为红的oplt.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‘)#绘制x，y轴plt.xlabel(‘Clump Thickness‘)plt.ylabel(‘Cell Size‘)plt.title(‘1-2‘)#显示图#plt.show()#导入import numpy as np#利用random函数随机采样直线的系数与截距intercept=np.random.random([1])coef=np.random.random([2])print coef,interceptlx=np.arange(0, 12)#创建等差数组ly=(-intercept-lx*coef[0])/coef[1]#截距式plt.plot(lx,ly,c=‘yellow‘)#绘随机直线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‘)#绘制x，y轴plt.xlabel(‘Clump Thickness‘)plt.ylabel(‘Cell Size‘)plt.title(‘1-3‘)#显示图plt.show()#导入sklearn的逻辑斯蒂回归分类器from sklearn.linear_model import LinearRegressionlr=LinearRegression()lr.fit(df_train[[‘Clump Thickness‘,‘Cell Size‘]][0:10],df_train[‘Type‘][0:10])print ‘Testing accuracy (10 training sample):‘,lr.score(df_test[[‘Clump Thickness‘ , ‘Cell Size‘]],df_test[‘Type‘])print "你好，中国"#第二次intercept=lr.intercept_coef=lr.coef_[:2]print coef,interceptly=(-intercept-lx*coef[0])/coef[1]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=150, c=‘black‘)#绘制x，y轴plt.xlabel(‘Clump Thickness‘)plt.ylabel(‘Cell Size‘)plt.title(‘1-4‘)#显示图plt.show()lr=LinearRegression()lr.fit(df_train[[‘Clump Thickness‘ , ‘Cell Size‘]][:10],df_train[‘Type‘][:10])print ‘Testing accuracy (all training sample):‘,lr.score(df_test[[‘Clump Thickness‘ , ‘Cell Size‘]],df_test[‘Type‘])#第三次intercept=lr.intercept_coef=lr.coef_[:2]ly=(-intercept-lx*coef[0])/coef[1]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‘)#绘制x，y轴plt.xlabel(‘Clump Thickness‘)plt.ylabel(‘Cell Size‘)plt.title(‘1-5‘)#显示图plt.show()print ‘end‘ |
2_1_2_1.py:
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# coding=utf-8#__author__ = ‘lenovo‘#线性回归器预测美国波士顿地区房价#从包中导入房价数据from sklearn.datasets import load_bostonboston=load_boston()print boston.DESCR#导入数据分割器from sklearn.cross_validation import train_test_splitimport numpy as np#导入有价值数据x=boston.datay=boston.target#print x,yx_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=33)#分析回归目标值的差异print "The max target value is",np.max(boston.target)print "The min target value is",np.min(boston.target)print "The average target value is",np.mean(boston.target)#有输出结果看目标值差异较大，需进行标准化处理from sklearn.preprocessing import StandardScaler#初始化特征和目标值的标准化器ss_x=StandardScaler()ss_y=StandardScaler()#对训练数据和测试数据标准化x_train=ss_x.fit_transform(x_train)#训练算法，设置内部参数,数据转换x_test=ss_x.transform(x_test)#数据转换y_train=ss_y.fit_transform(y_train)y_test=ss_y.transform(y_test)#使用LR与SGDRegression对房价进行预测from sklearn.linear_model import LinearRegressionlr=LinearRegression()#使用训练数据进行参数估计lr.fit(x_train,y_train)#对测试数据进行回归预测lr_y_predition=lr.predict(x_test)from sklearn.linear_model import SGDRegressorsgdr=SGDRegressor()sgdr.fit(x_train,y_train)sgdr_y_predict=sgdr.predict(x_test)#三种回归评价机制以及两种调用R-squared评价模块的方法，对本节模型的回归性能做出评价print ‘The avlue of default measurement of LinearRegression is‘,lr.score(x_test,y_test)#从sklearn.metrics依次导入r2_score,mean_squared_error,mean_absolute_errorfrom sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error#使用r2_score模块，并输出评估结果print ‘The value of R-Squared of LinearRegression is‘,r2_score(y_test,lr_y_predition)#使用mean_squared_error模块，并输出评估结果print ‘The mean squared error of LinearRegression is‘,mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(lr_y_predition))#使用mean_absolute_error模块，并输出评估结果print ‘The mean absoluate error of LinearRegression is‘,mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(lr_y_predition))#使用SGDRegressor模块自带的评估模块，并输出评估模块print ‘The value of default measurement of SGDRegressor is‘,sgdr.score(x_test,y_test)#使用r2_score模块，并输出评估结果print ‘The value of R-Squared of SGDRegressor is‘,r2_score(y_test,sgdr_y_predict)#使用mean_squared_error模块，并输出评估结果print ‘The mean squared error of SGDRegressor is‘,mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(sgdr_y_predict))#使用mean_absolute_error模块，并输出评估结果print ‘The mean absoluate error of SGDRegressor is‘,mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(sgdr_y_predict)) |
标签：body ror pytho ros head regress targe lib val
原文地址：http://www.cnblogs.com/stevendes1/p/6725877.html
