使用方法
使用python语言实现对于支持向量机（SVM）特征选择的实现，特征选择算法为f-score,该程序的主要有点是可输入文件囊括了csv,libsvm,arff等在序列分类的机器学习领域常用到的格式，其中csv:最后一列为class,libsvm:第一列为class,arff:通常最后一列为类别，其中csv和libsvm中不存在开头，直接是使用的数据。
python train.py -i 1.csv,2.csv,3.libsvm,4.arff -c 5
- 其中train.py为程序名称
- -i :后面接文件名，可以为csv,libsvm,arff格式，多个文件也可以用，但建议不要，因为特征选择时间通常很长
- -c:后面5代表五折交叉验证
#!/usr/bin/env python
# encoding:utf-8
import os
import sys
import getopt
import threading
import pandas as pd
import math
import numpy as np
from time import clock
from sklearn.feature_selection import f_classif
from sklearn.externals.joblib import Memory
from sklearn import metrics
import easy_excel
import itertools
from sklearn.model_selection import KFold
from sklearn import svm
from sklearn.model_selection import train_test_split
import math
from xgboost import XGBClassifier
from sklearn.neighbors import KNeighborsClassifier
import easy_excel
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import *
import sklearn.ensemble
from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
import sys
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
import subprocess
from sklearn.utils import shuffle
import itertools
from sklearn.ensemble import GradientBoostingClassifier
import sys
from sklearn.decomposition import PCA
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC, LinearSVC
from sklearn.naive_bayes import BernoulliNB
from sklearn.datasets import load_svmlight_file
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import SGDClassifier, LogisticRegression
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, \
BaggingClassifier, ExtraTreesClassifier, GradientBoostingClassifier
def performance(labelArr, predictArr):
#labelArr[i] is actual value,predictArr[i] is predict value
TP = 0.; TN = 0.; FP = 0.; FN = 0.
for i in range(len(labelArr)):
if labelArr[i] == 1 and predictArr[i] == 1:
TP += 1.
if labelArr[i] == 1 and predictArr[i] == 0:
FN += 1.
if labelArr[i] == 0 and predictArr[i] == 1:
FP += 1.
if labelArr[i] == 0 and predictArr[i] == 0:
TN += 1.
if (TP + FN)==0:
SN=0
else:
SN = TP/(TP + FN) #Sensitivity = TP/P and P = TP + FN
if (FP+TN)==0:
SP=0
else:
SP = TN/(FP + TN) #Specificity = TN/N and N = TN + FP
if (TP+FP)==0:
precision=0
else:
precision=TP/(TP+FP)
if (TP+FN)==0:
recall=0
else:
recall=TP/(TP+FN)
GM=math.sqrt(recall*SP)
#MCC = (TP*TN-FP*FN)/math.sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))
return precision,recall,SN,SP,GM,TP,TN,FP,FN
mem = Memory("./mycache")
@mem.cache
def get_data(file_name):
data = load_svmlight_file(file_name)
return data[0], data[1]
def csv_and_arff2svm(arff_files):
svm_files = []
for arff_file in arff_files:
name = arff_file[0: arff_file.rindex('.')]
tpe = arff_file[arff_file.rindex('.')+1:]
svm_file = name+".libsvm"
svm_files.append(svm_file)
if tpe == "arff":
if os.path.exists(svm_file):
pass
else:
f = open(arff_file)
w = open(svm_file, 'w')
flag = False
for line in f.readlines():
if flag:
if line.strip() == '':
continue
temp = line.strip('\n').strip('\r').split(',')
w.write(temp[len(temp)-1])
for i in range(len(temp)-1):
w.write(' '+str(i+1)+':'+str(temp[i]))
w.write('\n')
else:
line = line.upper()
if line.startswith('@DATA') or flag:
flag = True
f.close()
w.close()
elif tpe == "csv":
if os.path.exists(svm_file):
pass
else:
f = open(arff_file)
w = open(svm_file, 'w')
for line in f.readlines():
if line.strip() == '':
continue
temp = line.strip('\n').strip('\r').split(',')
w.write(temp[len(temp)-1])
for i in range(len(temp)-1):
w.write(' '+str(i+1)+':'+str(temp[i]))
w.write('\n')
f.close()
w.close()
elif tpe == "libsvm":
continue
else:
print "File format error! Arff and libsvm are passed."
sys.exit()
return svm_files
opts, args = getopt.getopt(sys.argv[1:], "hi:c:t:o:s:m:", )
for op, value in opts:
if op == "-i":
input_files = str(value)
input_files = input_files.replace(" ", "").split(',')
for input_file in input_files:
if input_file == "":
print "Warning: please insure no blank in your input files !"
sys.exit()
elif op == "-c":
cv = int(value)
if __name__ =="__main__":
path=""
outputname="svm_f-score"
name=outputname
print '*** Validating file format ...'
input_files = csv_and_arff2svm(input_files)
for input_file in input_files:
# 导入原始数据
X, Y = get_data(input_file)
train_data = X.todense()
train_data=np.array(train_data)
F, pval = f_classif(train_data, Y)
idx = np.argsort(F)
selected_list_=idx[::-1]
F_sort_value=[F[e] for e in selected_list_]
with open("all_dimension_results.txt",'a') as f:
f.write(str(F_sort_value)+"\n")
print F_sort_value
with open("all_dimension_results.txt",'a') as f:
f.write(str(selected_list_)+"\n")
print selected_list_
bestACC=0
bestC=0
bestgamma=0
best_dimension=0
all_dimension_results=[]
for select_num in xrange(1,len(train_data[0])+1):
train_data2=train_data
print np.array(train_data).shape
print np.array(train_data2).shape
selected_list_2=selected_list_[xrange(select_num)]
X_train=pd.DataFrame(train_data2)
X_train=X_train.iloc[:,selected_list_2]
X = np.array(X_train)
svc = svm.SVC()
parameters = {'kernel': ['rbf'], 'C':map(lambda x:2**x,np.linspace(-2,5,7)), 'gamma':map(lambda x:2**x,np.linspace(-5,2,7))}
clf = GridSearchCV(svc, parameters, cv=cv, n_jobs=2, scoring='accuracy')
clf.fit(X, Y)
C=clf.best_params_['C']
joblib.dump(clf,path+outputname+str(select_num)+".model")
gamma=clf.best_params_['gamma']
y_predict=cross_val_predict(svm.SVC(kernel='rbf',C=C,gamma=gamma),X,Y,cv=cv,n_jobs=2)
y_predict_prob=cross_val_predict(svm.SVC(kernel='rbf',C=C,gamma=gamma,probability=True),X,Y,cv=cv,n_jobs=2,method='predict_proba')
predict_save=[Y.astype(int),y_predict.astype(int),y_predict_prob[:,1]]
predict_save=np.array(predict_save).T
pd.DataFrame(predict_save).to_csv(path+outputname+"_"+'_predict_crossvalidation.csv',header=None,index=False)
ROC_AUC_area=metrics.roc_auc_score(Y,y_predict)
ACC=metrics.accuracy_score(Y,y_predict)
precision, recall, SN, SP, GM, TP, TN, FP, FN = performance(Y, y_predict)
F1_Score=metrics.f1_score(Y, y_predict)
F_measure=F1_Score
MCC=metrics.matthews_corrcoef(Y, y_predict)
pos=TP+FN
neg=FP+TN
savedata=[[['svm'+"C:"+str(C)+"gamma:"+str(gamma),ACC,precision, recall,SN, SP, GM,F_measure,F1_Score,MCC,ROC_AUC_area,TP,FN,FP,TN,pos,neg]]]
if ACC>bestACC:
bestACC=ACC
bestgamma=gamma
bestC=C
best_dimension=X.shape[1]
print savedata
print X.shape[1]
with open("all_dimension_results.txt",'a') as f:
f.write(str(savedata)+"\n")
all_dimension_results.append(savedata)
print bestACC
print bestC
print bestgamma
print best_dimension
easy_excel.save("svm_crossvalidation",[str(X.shape[1])],savedata,path+'cross_validation_'+name+'.xls')
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