SVM实践

# encoding=utf8
__author__ = ‘wang‘
# set the encoding of input file  utf-8
import sys
reload(sys)
sys.setdefaultencoding(‘utf-8‘)
import os
from svmutil import *
import subprocess

# get the data in svm format
featureStringCorpusDir = ‘./svmFormatFeatureStringCorpus/‘
fileList = os.listdir(featureStringCorpusDir)
# get the number of the files
numFile = len(fileList)
trainX = []
trainY = []
testX = []
testY = []
# enumrate the file
for i in xrange(numFile):
    # print i
    fileName = fileList[i]
    if(fileName.endswith(".libsvm")):
        # check the data format
　　　　 # 
        checkdata_py = r"./tool/checkdata.py"
        # the file name
        svm_file = featureStringCorpusDir + fileName
        cmd = ‘python {0} {1} ‘.format(checkdata_py, svm_file)
        # print ("excute ‘{}‘".format(cmd))
        # print(‘Check Data...‘)
        check_data = subprocess.Popen(cmd, shell = True, stdout = subprocess.PIPE)
        (stdout, stderr) = check_data.communicate()
        returncode = check_data.returncode
        if returncode == 1:
            print stdout
            exit(1)

        y, x = svm_read_problem(svm_file)
        trainX += x[:100]
        trainY += y[:100]
        testX += x[-1:-6:-1]
        testY += y[-1:-6:-1]

print testX
# train the c-svm model
m = svm_train(trainY, trainX, ‘-s 0 -c 4 -t 2 -g 0.1 -e 0.1‘)
# predict the test data
p_label, p_acc, p_val = svm_predict(testY, testX, m)
# output the result to the file
result_file = open("./result.txt","w")
result_file.write( "testY p_label p_val\n")
for c in xrange(len(p_label)):
    result_file.write( str(testY[c]) + " " + str(p_label[c]) + " " + str(p_val[c]) + "\n")
result_file.write( "accuracy: " + str(p_acc) + "\n")
result_file.close()
# print p_val

The format of training and testing data file is:

<label> <index1>:<value1> <index2>:<value2> ..
.
.
.

Each line contains an instance and is ended by a ‘\n‘ character.  For 
classification, <label> is an integer indicating the class label
(multi-class is supported)（如何label是个非数值的，将其转化为数值型）. For regression, <label> is 
the target value which can be any real number. For one-class SVM, it‘s not used
so can be any number.  The pair <index>:<value> gives a feature (attribute) value:
<index> is an integer starting from 1 and <value> is a real number. The only exception 
is the precomputed kernel, where <index> starts from 0; see the section of precomputed kernels. 
Indices must be in ASCENDING order（要升序排序）. Labels in the testing file are only used to calculate accuracy 
or errors. If they are unknown, just fill the first column with any numbers.

A sample classification data included in this package is `heart_scale‘. To check if your data is 
in a correct form, use `tools/checkdata.py‘ (details in `tools/README‘).

Type `svm-train heart_scale‘, and the program will read the training
data and output the model file `heart_scale.model‘. If you have a test
set called heart_scale.t, then type `svm-predict heart_scale.t
heart_scale.model output‘ to see the prediction accuracy. The `output‘
file contains the predicted class labels.

For classification, if training data are in only one class (i.e., all
labels are the same), then `svm-train‘ issues a warning message:
`Warning: training data in only one class. See README for details,‘
which means the training data is very unbalanced. The label in the
training data is directly returned when testing.

`svm-train‘ Usage
=================

Usage: svm-train [options] training_set_file [model_file]
options:
-s svm_type : set type of SVM (default 0)
	0 -- C-SVC		(multi-class classification)
	1 -- nu-SVC		(multi-class classification)
	2 -- one-class SVM	
	3 -- epsilon-SVR	(regression)
	4 -- nu-SVR		(regression)
-t kernel_type : set type of kernel function (default 2)
	0 -- linear: u‘*v
	1 -- polynomial: (gamma*u‘*v + coef0)^degree
	2 -- radial basis function: exp(-gamma*|u-v|^2)
	3 -- sigmoid: tanh(gamma*u‘*v + coef0)
	4 -- precomputed kernel (kernel values in training_set_file)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/num_features)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 100)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)  （当数据不平衡时，可给不同的类设置不用的惩罚）
-v n: n-fold cross validation mode
-q : quiet mode (no outputs)


The k in the -g option means the number of attributes in the input data.

option -v randomly splits the data into n parts and calculates cross
validation accuracy/mean squared error on them.

See libsvm FAQ for the meaning of outputs.

`svm-predict‘ Usage
===================

Usage: svm-predict [options] test_file model_file output_file
options:
-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported

model_file is the model file generated by svm-train.
test_file is the test data you want to predict.
svm-predict will produce output in the output_file.

*
optimization finished, #iter = 119
nu = 0.272950
obj = -122.758206, rho = -1.384603
nSV = 127, nBSV = 21
*

..............（此处省略部分）

*
optimization finished, #iter = 129
nu = 0.214275
obj = -89.691907, rho = -1.105131
nSV = 103, nBSV = 8
*
optimization finished, #iter = 77
nu = 0.147922
obj = -67.825431, rho = 0.984237
nSV = 80, nBSV = 10
Total nSV = 1246
Accuracy = 51.4286% (36/70) (classification)