如何使用 MATLAB 从 WEKA 检索类值

我正在尝试使用 MATLAB 和 WEKA API 从 WEKA 检索类。一切看起来都很好，但类始终为 0。有什么想法吗？

我的数据集有 241 个属性，将 WEKA 应用于该数据集我得到了正确的结果。

创建第一个训练和测试对象，然后构建分类器并执行分类实例。但这给出了错误的结果

    train = [xtrain ytrain];
    test =  [xtest];

    save ('train.txt','train','-ASCII');    
    save ('test.txt','test','-ASCII');

%## paths
WEKA_HOME = 'C:\Program Files\Weka-3-7';
javaaddpath([WEKA_HOME '\weka.jar']);

fName = 'train.txt';

%## read file

loader = weka.core.converters.MatlabLoader();

loader.setFile( java.io.File(fName) );
train = loader.getDataSet();
train.setClassIndex( train.numAttributes()-1 );

% setting class as nominal

v(1) = java.lang.String('-R');
v(2) = java.lang.String('242');
options = cat(1,v(1:end));

filter = weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions(options); 
filter.setInputFormat(train);   
train = filter.useFilter(train, filter);

fName = 'test.txt';

%## read file

loader = weka.core.converters.MatlabLoader();

loader.setFile( java.io.File(fName) );
test = loader.getDataSet();

%## dataset
relationName = char(test.relationName);
numAttr = test.numAttributes;
numInst = test.numInstances;

%## classification
classifier = weka.classifiers.trees.J48();
classifier.buildClassifier( train );
fprintf('Classifier: %s %s\n%s', ...
    char(classifier.getClass().getName()), ...
    char(weka.core.Utils.joinOptions(classifier.getOptions())), ...
    char(classifier.toString()) )

classes =[];

for i=1:numInst

     classes(i) = classifier.classifyInstance(test.instance(i-1));


end

这是一个新代码，但仍然无法正常工作 - 类 = 0。Weka 对于相同算法和数据集的输出正常

=== 按类别详细准确度 ===

               TP Rate   FP Rate   Precision   Recall  F-Measure   ROC Area  Class
                 0.99      0.015      0.985     0.99      0.988      0.991    0
                 0.985     0.01       0.99      0.985     0.988      0.991    1
Weighted Avg.    0.988     0.012      0.988     0.988     0.988      0.991
  

=== 混淆矩阵 ===

    a    b   <-- classified as
 1012   10 |    a = 0

   15 1003 |    b = 1
  

    ytest1 = ones(size(xtest,1),1); 

    train = [xtrain ytrain];
    test =  [xtest ytest1];

    save ('train.txt','train','-ASCII');    
    save ('test.txt','test','-ASCII');

%## paths
WEKA_HOME = 'C:\Program Files\Weka-3-7';
javaaddpath([WEKA_HOME '\weka.jar']);

fName = 'train.txt';

%## read file

loader = weka.core.converters.MatlabLoader();

loader.setFile( java.io.File(fName) );
train = loader.getDataSet();
train.setClassIndex( train.numAttributes()-1 );

v(1) = java.lang.String('-R');
v(2) = java.lang.String('242');
options = cat(1,v(1:end));

filter = weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions(options); 
filter.setInputFormat(train);   
train = filter.useFilter(train, filter);

fName = 'test.txt';

%## read file

loader = weka.core.converters.MatlabLoader();

loader.setFile( java.io.File(fName) );
test = loader.getDataSet();

filter = weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions( weka.core.Utils.splitOptions('-R last') );
filter.setInputFormat(test);   
test = filter.useFilter(test, filter);


%## dataset
relationName = char(test.relationName);
numAttr = test.numAttributes;
numInst = test.numInstances;

%## classification
classifier = weka.classifiers.trees.J48();

classifier.buildClassifier( train );
fprintf('Classifier: %s %s\n%s', ...
    char(classifier.getClass().getName()), ...
    char(weka.core.Utils.joinOptions(classifier.getOptions())), ...
    char(classifier.toString()) )

classes = zeros(numInst,1);
for i=1:numInst   
     classes(i) = classifier.classifyInstance(test.instance(i-1));     
end

这是Java中类分发的代码片段

// output predictions
    System.out.println("# - actual - predicted - error - distribution");
    for (int i = 0; i < test.numInstances(); i++) {
      double pred = cls.classifyInstance(test.instance(i));
      double[] dist = cls.distributionForInstance(test.instance(i));
      System.out.print((i+1));
      System.out.print(" - ");
      System.out.print(test.instance(i).toString(test.classIndex()));
      System.out.print(" - ");
      System.out.print(test.classAttribute().value((int) pred));
      System.out.print(" - ");
      if (pred != test.instance(i).classValue())
    System.out.print("yes");
      else
    System.out.print("no");
      System.out.print(" - ");
      System.out.print(Utils.arrayToString(dist));
      System.out.println();

我将它转换为这样的 MATLAB 代码

classes = zeros(numInst,1);
for i=1:numInst
     pred = classifier.classifyInstance(test.instance(i-1));  
     classes(i) = str2num(char(test.classAttribute().value(( pred))));
end

但类输出不正确。

在你的答案中，你没有表明 pred 包含类和 predProb 概率。只需打印即可！

训练和测试数据必须具有相同数量的属性。因此，在您的情况下，即使您不知道测试数据的实际类别，也只需使用虚拟值：

ytest = ones(size(xtest,1),1);    %# dummy class values for test data

train = [xtrain ytrain];
test =  [xtest ytest];

save ('train.txt','train','-ASCII');    
save ('test.txt','test','-ASCII');

加载测试数据集时，不要忘记将其转换为名义属性（就像对训练数据集所做的那样）：

filter = weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions( weka.core.Utils.splitOptions('-R last') );
filter.setInputFormat(test);   
test = filter.useFilter(test, filter);

最后，您可以调用经过训练的 J48 分类器来预测测试实例的类值：

classes = zeros(numInst,1);
for i=1:numInst
     classes(i) = classifier.classifyInstance(test.instance(i-1));
end

EDIT

如果不知道您正在使用的数据，就很难判断。

让我用一个完整的例子来说明。我将在 MATLAB 中根据 Fisher Iris 数据创建数据集（4 个属性、150 个实例、3 个类）。

%# load dataset (data + labels)
load fisheriris
X = meas;
Y = grp2idx(species);

%# partition the data into training/testing
c = cvpartition(Y, 'holdout',1/3);
xtrain = X(c.training,:);
ytrain = Y(c.training);
xtest = X(c.test,:);
ytest = Y(c.test);          %# or dummy values

%# save as space-delimited text file
train = [xtrain ytrain];
test =  [xtest ytest];
save train.txt train -ascii
save test.txt test -ascii

我应该在这里提到，在使用之前确保两个数据集中的每个数据集中都完全表示了类值，这一点很重要NumericToNominal筛选。否则，训练集和测试集可能是不相容。我的意思是，每个类中的每个类值都必须至少有一个实例。因此，如果您使用虚拟值，也许我们可以这样做：

ytest = ones(size(xtest,1),1);
v = unique(Y);
ytest(1:numel(v)) = v;

接下来，让我们使用 Weka API 读取新创建的文件。我们将最后一个属性从数字转换为名义（以启用分类）：

%# read train/test files using Weka
fName = 'train.txt';
loader = weka.core.converters.MatlabLoader();
loader.setFile( java.io.File(fName) );
train = loader.getDataSet();
train.setClassIndex( train.numAttributes()-1 );

fName = 'test.txt';
loader = weka.core.converters.MatlabLoader();
loader.setFile( java.io.File(fName) );
test = loader.getDataSet();
test.setClassIndex( test.numAttributes()-1 );

%# convert last attribute (class) from numeric to nominal
filter = weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions( weka.core.Utils.splitOptions('-R last') );
filter.setInputFormat(train);   
train = filter.useFilter(train, filter);

filter = weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions( weka.core.Utils.splitOptions('-R last') );
filter.setInputFormat(test);   
test = filter.useFilter(test, filter);

现在我们训练一个 J48 分类器并用它来预测测试实例的类别：

%# train a J48 tree
classifier = weka.classifiers.trees.J48();
classifier.setOptions( weka.core.Utils.splitOptions('-c last -C 0.25 -M 2') );
classifier.buildClassifier( train );

%# classify test instances
numInst = test.numInstances();
pred = zeros(numInst,1);
predProbs = zeros(numInst, train.numClasses());
for i=1:numInst
     pred(i) = classifier.classifyInstance( test.instance(i-1) );
     predProbs(i,:) = classifier.distributionForInstance( test.instance(i-1) );
end

最后，我们根据测试数据评估经过训练的模型性能（这应该与您在 Weka Explorer 中看到的类似）。显然，只有当测试实例具有真实的类值（而不是虚拟值）时，这才有意义：

eval = weka.classifiers.Evaluation(train);

eval.evaluateModel(classifier, test, javaArray('java.lang.Object',1));

fprintf('=== Run information ===\n\n')
fprintf('Scheme: %s %s\n', ...
    char(classifier.getClass().getName()), ...
    char(weka.core.Utils.joinOptions(classifier.getOptions())) )
fprintf('Relation: %s\n', char(train.relationName))
fprintf('Instances: %d\n', train.numInstances)
fprintf('Attributes: %d\n\n', train.numAttributes)

fprintf('=== Classifier model ===\n\n')
disp( char(classifier.toString()) )

fprintf('=== Summary ===\n')
disp( char(eval.toSummaryString()) )
disp( char(eval.toClassDetailsString()) )
disp( char(eval.toMatrixString()) )

上述示例在 MATLAB 中的输出：

=== Run information ===

Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: train.txt-weka.filters.unsupervised.attribute.NumericToNominal-Rlast
Instances: 100
Attributes: 5

=== Classifier model ===

J48 pruned tree
------------------

att_4 <= 0.6: 1 (33.0)
att_4 > 0.6
|   att_3 <= 4.8
|   |   att_4 <= 1.6: 2 (32.0)
|   |   att_4 > 1.6: 3 (3.0/1.0)
|   att_3 > 4.8: 3 (32.0)

Number of Leaves  :     4

Size of the tree :  7

=== Summary ===

Correctly Classified Instances          46               92      %
Incorrectly Classified Instances         4                8      %
Kappa statistic                          0.8802
Mean absolute error                      0.0578
Root mean squared error                  0.2341
Relative absolute error                 12.9975 %
Root relative squared error             49.6536 %
Coverage of cases (0.95 level)          92      %
Mean rel. region size (0.95 level)      34      %
Total Number of Instances               50     

=== Detailed Accuracy By Class ===

             TP Rate  FP Rate  Precision   Recall  F-Measure   ROC Area  Class
              1        0         1         1         1          1        1
              0.765    0         1         0.765     0.867      0.879    2
              1        0.118     0.8       1         0.889      0.938    3
Weighted Avg. 0.92     0.038     0.936     0.92      0.919      0.939

=== Confusion Matrix ===

  a  b  c   <-- classified as
 17  0  0 |  a = 1
  0 13  4 |  b = 2
  0  0 16 |  c = 3