java - Weka:属性选择期间的监督离散化问题和错误 "Not enough training instances"

Question

在过去的一个月左右的时间里，我一直在自学 Weka API(我是一名学生)。我正在做的是编写一个程序，它将过滤一组特定的数据并最终为其构建一个贝叶斯网，一周前我已经完成了我的离散化类和属性选择类。就在几天前，我意识到我需要将我的离散化函数更改为受监督，并最终使用默认的 Fayyad＆Irani 方法，在我这样做之后，我开始在我的属性选择类中收到此错误:

Exception in thread "main" weka.core.WekaException: 
weka.attributeSelection.CfsSubsetEval: Not enough training instances with class labels (required: 1, provided: 0)!
at weka.core.Capabilities.test(Capabilities.java:1138)
at weka.core.Capabilities.test(Capabilities.java:1023)
at weka.core.Capabilities.testWithFail(Capabilities.java:1302)
at weka.attributeSelection.CfsSubsetEval.buildEvaluator(CfsSubsetEval.java:331)
at weka.attributeSelection.AttributeSelection.SelectAttributes(AttributeSelection.java:597)
at weka.filters.supervised.attribute.AttributeSelection.batchFinished(AttributeSelection.java:456)
at weka.filters.Filter.useFilter(Filter.java:663)
at AttributeSelectionFilter.selectionFilter(AttributeSelectionFilter.java:29)
at Runner.main(Runner.java:70)

我在更改之前的属性选择工作得很好，所以我认为我可能在我的离散类中做错了什么。我这个问题的另一部分与此相关，因为我还注意到我的离散化类似乎并没有真正离散化数据；它只是将所有数字数据放入一个范围内，而不是像 Fayyad 和Irani 那样策略性地将其分箱。

这是我的离散类:

import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.supervised.attribute.Discretize;
import weka.filters.unsupervised.attribute.NumericToNominal;

public class DiscretizeFilter
{
    private Instances data;
    private boolean sensitiveOption;
    private Filter filter = new Discretize();

    public DiscretizeFilter(Instances data, boolean sensitiveOption)
    {
        this.data = data;
        this.sensitiveOption = sensitiveOption;
    }

    public Instances discreteFilter() throws Exception
    {
        NumericToNominal nm = new NumericToNominal();
        nm.setInputFormat(data);
        Filter.useFilter(data, nm);
        Instances nominalData = nm.getOutputFormat();

        if(sensitiveOption)//if the user wants extra sensitivity
        {
            String options[] = new String[1];
            options[0] = options[0];
            options[2] = "-E";
            ((Discretize) filter).setOptions(options);
        }
        filter.setInputFormat(nominalData);
        Filter.useFilter(nominalData,filter);
        return filter.getOutputFormat();
    }
}

这是我的属性选择类:

import weka.attributeSelection.BestFirst;
import weka.attributeSelection.CfsSubsetEval;
import weka.core.Instances;
import weka.filters.supervised.attribute.AttributeSelection;

public class AttributeSelectionFilter 
{
    public Instances selectionFilter(Instances data) throws Exception
    {
        AttributeSelection filter = new AttributeSelection();

        for(int i = 0; i < data.numInstances(); i++)
        {
            filter.input(data.instance(i));
        }
        CfsSubsetEval eval = new CfsSubsetEval();
        BestFirst search = new BestFirst();
        filter.setSearch(search);
        filter.setEvaluator(eval);

        filter.setInputFormat(data);
        AttributeSelection.useFilter(data, filter);

        return filter.getOutputFormat();
    }

    public int attributeCounter(Instances data)
    {
        return data.numAttributes();
    }
}

任何帮助将不胜感激!!!

Answer 1

Weka 在内部将属性值存储为 double 值。似乎抛出了异常，因为数据集中的每个实例 (data) 都“缺少类”，即出于某种原因被赋予了内部类属性值 NaN(“不是数字”) 。我建议仔细检查data's类属性是否已正确创建/设置。