Question

我使用python中的libsvm（svmutils）进行分类任务。分类器是准确的。但是，我得到这样的输出：

*
optimization finished, #iter = 75
nu = 0.000021
obj = -0.024330, rho = 0.563710
nSV = 26, nBSV = 0
Total nSV = 26
*
optimization finished, #iter = 66
nu = 0.000030
obj = -0.035536, rho = -0.500676
nSV = 21, nBSV = 0
Total nSV = 21
*
optimization finished, #iter = 78
nu = 0.000029
obj = -0.033921, rho = -0.543311
nSV = 23, nBSV = 0
Total nSV = 23
*
optimization finished, #iter = 90
nu = 0.000030
obj = -0.035333, rho = -0.634721
nSV = 23, nBSV = 0
Total nSV = 23
Accuracy = 0% (0/1) (classification)
Accuracy = 0% (0/1) (classification)
Accuracy = 0% (0/1) (classification)
Accuracy = 0% (0/1) (classification)

有什么方法可以抑制这个对话框吗？分类器非常好，我很好奇。另外，"Accuracy"代表什么？为什么在我的情况下这是0％？（数据在80个维度中不重叠。总共4个类。我也正确地将其标准化。）

Answer 1

使用-q参数选项

import svmutil
param = svmutil.svm_parameter('-q')
...

或

import svmutil
x = [[0.2, 0.1], [0.7, 0.6]]
y = [0, 1]
svmutil.svm_train(y, x, '-q')

Answer 2

这可行：

import sys
from StringIO import StringIO

# back up your standard output
bkp_stdout = sys.stdout

# replace standard output with dummy stream
sys.stdout = StringIO()
print 1  # here you should put you call (classification)

#restore standard output for further use
sys.stdout = bkp_stdout
print 2

此外，在分类问题中，准确度是使用训练模型从测试/交叉验证集中正确预测项目的一部分（百分比）。

Answer 3

要抑制训练和预测输出，您需要结合has2k1（用于抑制训练输出）和vonPetrushev（用于抑制预测输出）提供的解决方案。

不幸的是，您无法执行以下操作：

# Test matrix built, execute prediction.
paramString = "" if useVerbosity else " -q "
predLabels, predAccuracy, predDiscriminants = \
 svmutil.svm_predict( targetLabels, testData, svModel.representation, paramString )

因为使用当前的python接口，您将收到以下错误：

  File "/home/jbbrown/local_bin/pyLibSVM/pyLibSVM/svmutil.py", line 193, in svm_predict
    raise ValueError("Wrong options")
  ValueError: Wrong options

抑制libsvm（python）中的输出

3 个答案: