用sklearn进行线性分类

这里通过LogisticRegression分类器和随机梯度分类器对一些肿瘤数据进行分类，分出是良性还是恶性
代码来自《python机器学习及实践》

#pandas用于处理数据
import pandas as pd                                                         
import numpy as np
#将数据分割为训练集和测试集
from sklearn import  train_test_split
#用于标准化数据                                            
from sklearn.preprocessing import StandardScaler
#一种线性分类技术                   
from sklearn.linear_model import LogisticRegression
#一种线性分类技术           
from sklearn.linear_model import SGDClassifier  
#用于分析数据                 
from sklearn.metrics import classification_report                           
#所有的属性和编号，标签
colum_names = ['Sample code number',
               'Clump Thickness',
               'Uniformity of Cell Size',
               'Uniformity of Cell Shape',
               'Marginal Adhesion',
               'Single Epithelial Cell Size',
               'Bare Nuclei', 'Bland Chromatin',
               'Normal Nucleoli',
               'Mitoses',
                'Class']
#从csv文件中读入数据                                                            
data = pd.read_csv('data.csv', names = colum_names)                             
#将?用nan(非数字)替代
data = data.replace(to_replace='?', value=np.nan)
#放弃含有nan的数据                                 
data = data.dropna(how='any')                                                       
X_train, X_test, y_train, y_test = 
"""属性为每行的1-9号值（左闭右开）
label为每行的第11个属性
测试集占25%
随机数的种子为33
"""
train_test_split(data[colum_names[1:10]],                                       data[colum_names[10]],             
test_size=0.25,                                         random_state=33)                

ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.fit_transform(X_test)
lr = LogisticRegression()
sgdc = SGDClassifier()
lr.fit(X_train, y_train)
lr_y_predict = lr.predict(X_test)                                                               #用fit之后的结果对X_test进行预测
sgdc.fit(X_train, y_train)
sgdc_y_predict = sgdc.predict(X_test)
#对X_test的预测结果与y_test进行对比
print('Accuracy of LR Classifier:', lr.score(X_test, y_test))
#输出y_test与预测结果的对比                               
print(classification_report(y_test, lr_y_predict, target_names=['Benign', 'Malignant']))        
print('Accuarcy of SGD Classifier:', sgdc.score(X_test, y_test))
print(classification_report(y_test, sgdc_y_predict, target_names=['Benign', 'Malignant']))