前言

此程序基于良/恶性肿瘤预测实验。

分别用LogisticRegression模型和SGDClassifier模型实现预测任务。

本程序可以流畅运行于Python3.6环境，但是Python2.x版本需要修正的地方也已经在注释中说明。

requirements:pandas,numpy,scikit-learn

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实验结果分析

LogisticRegression比起SGDClassifier在测试机上表现有更高的准确性，这是因为Scikit-learn中采用解析的方式精确计算LogisticRegression的参数，而使用梯度法估计SGDClassifier的参数。

相比之下，前者计算时间长但是模型性能略高；后者采用随机梯度上升算法估计模型参数，计算时间短，但是产出的模型性能略低。一般而言，对于训练数据规模在10万量级以上的数据，考虑到时间的耗用，更适合使用随机梯度算法对模型进行估计。

程序源码

import pandas as pd

import numpy as np

# features column names

column_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']

#read data from csv file

data = pd.read_csv('./breast-cancer-wisconsin.data',names=column_names)

#Data preprocessing

#replace all ? with standard missing value

data = data.replace(to_replace='?',value=np.nan)

#drop all data rows which have any missing feature

data=data.dropna(how='any')

# data.to_csv('./text.csv')# save data to csv file

#notes:you should use cross_valiation instead of model_valiation in python 2.7

#from sklearn.cross_validation import train_test_split #DeprecationWarning

from sklearn.model_selection import train_test_split #use train_test_split module of sklearn.model_valiation to split data

#take 25 percent of data randomly for testing,and others for training

X_train,X_test,y_train,y_test = train_test_split(data[column_names[1:10]],data[column_names[10]],test_size=0.25,random_state=33)

#check the numbers and category distribution of the test samples

# print(y_train.value_counts())

# print(y_test.value_counts())

#import relative package

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LogisticRegression

from sklearn.linear_model import SGDClassifier

#standardizing data in train set and test set

ss = StandardScaler()

X_train = ss.fit_transform(X_train)

X_test = ss.transform(X_test)

#initializing logisticregression and sgdcclassifier model

lr=LogisticRegression()

#notes:the default parameters in python2.7 are max_iter=5 tol=none,you can don't specify the parameters of sgdclassifier

#sgdc=SGDClassifier() #DeprecationWarning

sgdc=SGDClassifier(max_iter=5,tol=None)

#call fit function to trainning arguments ofmodel

lr.fit(X_train,y_train)

#save the prediction of test set in variable

lr_y_predict=lr.predict(X_test)

sgdc.fit(X_train,y_train)

sgdc_y_predict=sgdc.predict(X_test)

#performance analysis

from sklearn.metrics import classification_report

#get accuracy by the score function in LR model

print('Accuracy of LR Classifier:',lr.score(X_test,y_test))

#get  precision ,recall and f1-score from classification_report module

print(classification_report(y_test,lr_y_predict,target_names=['Benign','Malignant']))

#get accuracy by the score function in SGD classifier

print('Accuracy of SGD Classifier:',sgdc.score(X_test,y_test))

#get  precision ,recall and f1-score from classification_report module

print(classification_report(y_test,sgdc_y_predict,target_names=['Benign','Malignant']))

Ubuntu16.04 Python3.6 程序输出结果:

Accuracy of LR Classifier: 0.9883040935672515

            precision    recall  f1-score  support

    Benign      0.99      0.99      0.99      100

  Malignant      0.99      0.99      0.99        71

avg / total      0.99      0.99      0.99      171

Accuracy of SGD Classifier: 0.9824561403508771

            precision    recall  f1-score  support

    Benign      1.00      0.97      0.98      100

  Malignant      0.96      1.00      0.98        71

avg / total      0.98      0.98      0.98      171

数据下载地址

欢迎指正错误，包括英语和程序错误。有问题也欢迎提问，一起加油一起进步。

本程序完全是本人逐字符输入的劳动结果，转载请注明出处。