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使用交叉验证和F1分数选择SVM参数

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我需要在SVM中调整C＆Sigma时跟踪F1分数，例如以下代码跟踪准确度，我需要将其更改为F1-Score但我无法做到......

%# read some training data
[labels,data] = libsvmread('./heart_scale');

%# grid of parameters
folds = 5;
[C,gamma] = meshgrid(-5:2:15, -15:2:3);

%# grid search, and cross-validation
cv_acc = zeros(numel(C),1);
    for i=1:numel(C)
cv_acc(i) = svmtrain(labels, data, ...
                sprintf('-c %f -g %f -v %d', 2^C(i), 2^gamma(i), folds));
end

%# pair (C,gamma) with best accuracy
[~,idx] = max(cv_acc);

%# now you can train you model using best_C and best_gamma
best_C = 2^C(idx);
best_gamma = 2^gamma(idx);
%# ...

我看过以下两个链接

Retraining after Cross Validation with libsvm

10 fold cross-validation in one-against-all SVM (using LibSVM)

我知道我必须首先在训练数据中找到最佳的C和gamma / sigma参数，然后使用这两个值进行LEAVE-ONE-OUT交叉验证分类实验，所以我现在想要的是先做一个网格 - 调整C＆sigma的研究 . 请我更喜欢使用MATLAB-SVM而不是LIBSVM . 以下是我的LEAVE-ONE-OUT交叉验证分类代码 .

... clc
 clear all
close all
a = load('V1.csv');
X = double(a(:,1:12));
Y = double(a(:,13));
% train data
datall=[X,Y];
A=datall;
n = 40;
ordering = randperm(n);
B = A(ordering, :);  
good=B; 
input=good(:,1:12);
target=good(:,13);
CVO = cvpartition(target,'leaveout',1);  
cp = classperf(target);                      %# init performance tracker
svmModel=[];
for i = 1:CVO.NumTestSets                                %# for each fold
trIdx = CVO.training(i);              
teIdx = CVO.test(i);                   
%# train an SVM model over training instances

svmModel = svmtrain(input(trIdx,:), target(trIdx), ...
       'Autoscale',true, 'Showplot',false, 'Method','ls', ...
      'BoxConstraint',0.1, 'Kernel_Function','rbf', 'RBF_Sigma',0.1);
%# test using test instances
pred = svmclassify(svmModel, input(teIdx,:), 'Showplot',false);
%# evaluate and update performance object
cp = classperf(cp, pred, teIdx); 
end
%# get accuracy
accuracy=cp.CorrectRate*100
sensitivity=cp.Sensitivity*100
specificity=cp.Specificity*100
PPV=cp.PositivePredictiveValue*100
NPV=cp.NegativePredictiveValue*100
%# get confusion matrix
%# columns:actual, rows:predicted, last-row: unclassified instances
cp.CountingMatrix
recallP = sensitivity;
recallN = specificity;
precisionP = PPV;
precisionN = NPV;
f1P = 2*((precisionP*recallP)/(precisionP + recallP));
f1N = 2*((precisionN*recallN)/(precisionN + recallN));
aF1 = ((f1P+f1N)/2);

我已经改变了代码，但我犯了一些错误，我收到了错误，

a = load('V1.csv');
X = double(a(:,1:12));
Y = double(a(:,13));
% train data
datall=[X,Y];
A=datall;
n = 40;
ordering = randperm(n);
B = A(ordering, :);  
good=B; 
inpt=good(:,1:12);
target=good(:,13);
k=10;
cvFolds = crossvalind('Kfold', target, k);   %# get indices of 10-fold CV
cp = classperf(target);                      %# init performance tracker
svmModel=[];
for i = 1:k 
    testIdx = (cvFolds == i);    %# get indices of test    instances
trainIdx = ~testIdx;   
C = 0.1:0.1:1; 
S = 0.1:0.1:1; 
fscores = zeros(numel(C), numel(S)); %// Pre-allocation
for c = 1:numel(C)   
for s = 1:numel(S)
    vals = crossval(@(XTRAIN, YTRAIN, XVAL, YVAL)(fun(XTRAIN, YTRAIN, XVAL, YVAL, C(c), S(c))),inpt(trainIdx,:),target(trainIdx));
    fscores(c,s) = mean(vals);
end
end
 end

[cbest, sbest] = find(fscores == max(fscores(:)));
C_final = C(cbest);
S_final = S(sbest);

.......

和功能.....

.....
function fscore = fun(XTRAIN, YTRAIN, XVAL, YVAL, C, S)
svmModel = svmtrain(XTRAIN, YTRAIN, ...
   'Autoscale',true, 'Showplot',false, 'Method','ls', ...
  'BoxConstraint', C, 'Kernel_Function','rbf', 'RBF_Sigma', S);

   pred = svmclassify(svmModel, XVAL, 'Showplot',false);

   cp = classperf(YVAL, pred)
   %# get accuracy
    accuracy=cp.CorrectRate*100
    sensitivity=cp.Sensitivity*100
    specificity=cp.Specificity*100
    PPV=cp.PositivePredictiveValue*100
    NPV=cp.NegativePredictiveValue*100
    %# get confusion matrix
    %# columns:actual, rows:predicted, last-row: unclassified instances
    cp.CountingMatrix
    recallP = sensitivity;
    recallN = specificity;
    precisionP = PPV;
    precisionN = NPV;
    f1P = 2*((precisionP*recallP)/(precisionP + recallP));
    f1N = 2*((precisionN*recallN)/(precisionN + recallN));
    fscore = ((f1P+f1N)/2);

    end
matlab machine-learning classification svm cross-validation

2 回答

  • 1

    所以基本上你想要采用你的这一行：

    svmModel = svmtrain(input(trIdx,:), target(trIdx), ...
       'Autoscale',true, 'Showplot',false, 'Method','ls', ...
      'BoxConstraint',0.1, 'Kernel_Function','rbf', 'RBF_Sigma',0.1);

    将它放在一个改变 'BoxConstraint' 和 'RBF_Sigma' 参数的循环中，然后使用crossval输出该迭代参数组合的f1分数 .

    您可以像在libsvm代码示例中一样使用单个for循环（即使用 meshgrid 和 1:numel() ，这可能更快）或嵌套的for循环 . 我将使用嵌套循环，以便您有两种方法：

    C = [0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100, 300] %// you must choose your own set of values for the parameters that you want to test. You can either do it this way by explicitly typing out a list
S = 0:0.1:1 %// or you can do it this way using the : operator
fscores = zeros(numel(C), numel(S)); %// Pre-allocation
for c = 1:numel(C)   
    for s = 1:numel(S)
        vals = crossval(@(XTRAIN, YTRAIN, XVAL, YVAL)(fun(XTRAIN, YTRAIN, XVAL, YVAL, C(c), S(c)),input(trIdx,:),target(trIdx));
        fscores(c,s) = mean(vals);
    end
end

%// Then establish the C and S that gave you the bet f-score. Don't forget that c and s are just indexes though!
[cbest, sbest] = find(fscores == max(fscores(:)));
C_final = C(cbest);
S_final = S(sbest);

    现在我们只需要定义函数 fun . 文档有关于 fun 的说法：

    fun是具有两个输入的函数的函数句柄，X，XTRAIN的训练子集和X，XTEST的测试子集，如下所示：testval = fun（XTRAIN，XTEST）每次调用它时，都应该使用fun XTRAIN拟合模型，然后使用该拟合模型返回在XTEST上计算的一些标准testval .

    所以 fun 需要：

    • 输出一个单独的f分数

    • 将X和Y的训练和测试集作为输入 . 请注意，这些都是实际训练集的子集！可以把它们想象成训练集的训练和验证SUBSET . 另请注意，crossval将为您分割这些设置！

    • 在训练子集上训练分类器（使用循环中的当前 C 和 S 参数）

    • 在测试（或验证相当）子集上运行新分类器

    • 计算并输出性能指标（在您的情况下，您希望获得f1分数）

    你会注意到 fun 可以't take any extra parameters which is why I'将它包装在一个匿名函数中，这样我们就可以传递当前的 C 和 S 值 . （即上面所有的 @(...)(fun(...)) . 这只是一个把我们的六个参数 fun 变成4参数的技巧 crossval 要求的一个 .

    function fscore = fun(XTRAIN, YTRAIN, XVAL, YVAL, C, S)

   svmModel = svmtrain(XTRAIN, YTRAIN, ...
       'Autoscale',true, 'Showplot',false, 'Method','ls', ...
      'BoxConstraint', C, 'Kernel_Function','rbf', 'RBF_Sigma', S);

   pred = svmclassify(svmModel, XVAL, 'Showplot',false);

   CP = classperf(YVAL, pred)

   fscore = ... %// You can do this bit the same way you did earlier
end
    回复于
  • 0

    我找到了 target(trainIdx) 的唯一问题 . 它是一个行向量，所以我只用 target(trainIdx) 替换了 target(trainIdx) 这是一个列向量 .

    回复于

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