如何使用MATLAB从WEKA检索类值 [英] How to retrieve class values from WEKA using MATLAB
问题描述
我正在尝试使用MATLAB和WEKA API从WEKA检索类.一切看起来不错，但类始终为0.任何想法??
I'm trying to retrieve classes from WEKA using MATLAB and WEKA API. All looks fine but classes are always 0. Any idea ??
我的数据集有241个属性，将WEKA应用于该数据集，我获得了正确的结果.
My data set has 241 atributes, applying WEKA to this dataset I'm obtaining correct results.
创建第一个训练和测试对象，而不是构建分类器并执行classifyInstance.但这会给出错误的结果
1st train and test objects are created than classifier is build and classifyInstance performed. But this give wrong result
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-classes =0.对于相同的算法和数据集，来自Weka的输出还可以
Here is a new code but still not working - classes = 0. Output from Weka for the same algo and data set is OK
===详细的分类精度===
=== Detailed Accuracy By Class ===
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===
=== Confusion Matrix ===
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
这是用于Java中的类分发的代码段
here is a code snippet for class distribution in Java
// 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();
我将其转换为这样的MATLAB代码
I converted it to MATLAB code like this
classes = zeros(numInst,1);
for i=1:numInst
pred = classifier.classifyInstance(test.instance(i-1));
classes(i) = str2num(char(test.classAttribute().value(( pred))));
end
但是类输出不正确.
在您的答案中，您没有显示pred包含类和predProb概率.只需打印就可以了！
In your answer you dont show that pred contains classes and predProb probabilities. Just print it !!!
推荐答案
培训和测试数据必须具有相同数量的属性.因此，就您而言，即使您不知道测试数据的实际类别，也只需使用伪值即可:
Training and testing data must have the same number of attributes. So in your case, even if you don't know the actual class of the test data, just use dummy values:
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');
在加载测试数据集时(就像对训练数据集所做的一样)，别忘了将其转换为名义属性:
Don't forget to convert it to a nominal attribute when you load the test dataset (like you did for the training dataset):
filter = weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions( weka.core.Utils.splitOptions('-R last') );
filter.setInputFormat(test);
test = filter.useFilter(test, filter);
最后，您可以调用训练有素的J48分类器来预测测试实例的类值:
Finally, you can call the trained J48 classifier to predict the class values for the test instances:
classes = zeros(numInst,1);
for i=1:numInst
classes(i) = classifier.classifyInstance(test.instance(i-1));
end
编辑
在不知道您正在使用的数据的情况下很难分辨.
EDIT
It is difficult to tell without knowing the data you are working with..
所以让我用一个完整的例子来说明.我将根据Fisher Fisher数据(4个属性，150个实例，3个类)在MATLAB中创建数据集.
So let me illustrate with a complete example. I am going to be creating the datasets in MATLAB out of the Fisher Iris data (4 attributes, 150 instances, 3 classes).
%# 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
在这里我要提到，在使用NumericToNominal过滤器之前，必须确保在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.否则，训练集和测试集可能不兼容.我的意思是，每个类中的每个类值中都必须至少有一个实例.因此，如果您使用的是虚拟值，也许我们可以这样做:
I should mention here that it is important to make sure that the class values are fully represented in each of the two datasets before using the NumericToNominal filter. Otherwise, the train and test sets could be incompatible. What I mean is that you must have at least one instance from every class value in each. Thus if you are using dummy values, maybe we can do this:
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(以进行分类):
Next, lets read the newly created files using Weka API. We convert the last attribute from numeric to nominal (to enable classification):
%# 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，并使用它来预测测试实例的类:
Now we train a J48 classifier and use it to predict the class of the test instances:
%# 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的性能(该外观应与您在Weka Explorer中看到的相似).显然，这仅在测试实例具有真实的类值(而非虚拟值)的情况下才有意义:
Finally, we evaluate the trained model performance over the test data (this should look similar to what you see in Weka Explorer). Obviously this only makes sense if the test instances have the true class value (not dummy values):
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()) )
上面示例中MATLAB的输出:
The output in MATLAB for the example above:
=== 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
这篇关于如何使用MATLAB从WEKA检索类值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
