如何将Weka Instances类属性值水平存储以统计分类标签出现次数

你当前代码每次遍历单条样本时，都会在texted()方法内创建新的HashMap，仅统计当前单条样本的类标签，自然每个标签统计结果都是1。

这里给两种可行方案：

方案1：按需求先拼接所有标签再统计 #

先把所有类标签拼接成一个空格分隔的长字符串，再调用你原有的词频统计逻辑，代码修改如下：

public class testDistributionClass {
    public static Instances loadData(String location) {
        try {
            return DataSource.read(location);
        }
        catch (Exception e) {
            System.err.println("Failed to load data from: " + location);
            e.printStackTrace();
            return null;
        }
    }

    HashMap<String, Integer> texted(String tex) {
        HashMap<String, Integer> counts = new HashMap<String, Integer>();
        for (String word : tex.split(" ")) {
            if (!counts.containsKey(word)) counts.put(word, 0);
            counts.put(word, counts.get(word) + 1);
        }
        counts.forEach((string, integer) -> System.out.printf("amount of \"%s\": %d\n", string, integer));
        return counts;
    }

    public static void main(String[] args) throws Exception {
        String test1 = "D:/Week 1/Arsenal_test.arff";
        Instances test = testDistributionClass.loadData(test1);
        test.setClassIndex(test.numAttributes()-1);

        Classifier testModel = (Classifier)new NaiveBayes();
        testModel.buildClassifier(test);

        testDistributionClass splitWords = new testDistributionClass();
        // 新增：拼接所有类标签的StringBuilder
        StringBuilder allLabels = new StringBuilder();
        for(int i = 0; i<test.numInstances();i++) {
            String trueClassLabel = test.instance(i).toString(test.classIndex());
            // 拼接标签，末尾加空格分隔
            allLabels.append(trueClassLabel).append(" ");
        }
        // 循环结束后统一统计
        System.out.println(splitWords.texted(allLabels.toString()));
    }
}

方案2：更高效的直接统计（无需拼接字符串） #

省略字符串拼接、切割步骤，直接用全局HashMap统计标签出现次数，性能更好：

public class testDistributionClass {
    public static Instances loadData(String location) {
        try {
            return DataSource.read(location);
        }
        catch (Exception e) {
            System.err.println("Failed to load data from: " + location);
            e.printStackTrace();
            return null;
        }
    }

    public static void main(String[] args) throws Exception {
        String test1 = "D:/Week 1/Arsenal_test.arff";
        Instances test = testDistributionClass.loadData(test1);
        test.setClassIndex(test.numAttributes()-1);

        Classifier testModel = (Classifier)new NaiveBayes();
        testModel.buildClassifier(test);

        HashMap<String, Integer> labelCounts = new HashMap<>();
        for(int i = 0; i<test.numInstances();i++) {
            String trueClassLabel = test.instance(i).toString(test.classIndex());
            // 直接统计
            labelCounts.put(trueClassLabel, labelCounts.getOrDefault(trueClassLabel, 0)+1);
        }
        // 输出结果
        labelCounts.forEach((label, count) -> System.out.printf("amount of \"%s\": %d\n", label, count));
        System.out.println(labelCounts);
    }
}

两种方案都可以得到你需要的聚合统计结果，比如输出{Win=7, Loss=5, Draw=2}这类聚合值。

内容的提问来源于stack exchange，提问作者Stackbeans