我需要跟踪F1分数，同时调整C＆amp; SVM中的Sigma， 例如，以下代码记录准确性，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)
我明白我必须首先在训练数据上找到最佳的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首先进行网格搜索以调整C＆amp;西格玛。 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);
我已经更改了代码 但我犯了一些错误而且我遇到了错误，
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
答案 0 :(得分：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);
将其置于一个循环中，该循环会改变您的'BoxConstraint'和'RBF_Sigma'参数，然后使用crossval输出该迭代参数组合的f1分数。
您可以使用与libsvm代码示例完全相同的单个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，以便您同时使用两种方法：
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。关于fun：
fun是具有两个输入的功能的函数句柄，即训练 X，XTRAIN的子集，以及X，XTEST的测试子集，如下所示：
testval = fun（XTRAIN，XTEST）每次调用它时，应该使用fun XTRAIN适合模型，然后返回计算的一些标准testval 使用该拟合模型的XTEST。
所以fun需要：
C和S参数）您会注意到fun无法使用任何额外参数，这就是我将其包装在匿名函数中的原因，以便我们可以传递当前C和S 。（即所有@(...)(fun(...))以上内容。这只是将我们的六个参数fun“转换”为crossval所需的4参数的技巧。
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
答案 1 :(得分：0)
我发现target(trainIdx)唯一的问题。这是一个行向量，所以我只是将target(trainIdx)替换为target(trainIdx)这是一个列向量。