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Java Evaluation.areaUnderROC方法代码示例

本文整理汇总了Java中weka.classifiers.Evaluation.areaUnderROC方法的典型用法代码示例。如果您正苦于以下问题:Java Evaluation.areaUnderROC方法的具体用法?Java Evaluation.areaUnderROC怎么用?Java Evaluation.areaUnderROC使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在weka.classifiers.Evaluation的用法示例。


在下文中一共展示了Evaluation.areaUnderROC方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Java代码示例。

示例1: getMetricScore

import weka.classifiers.Evaluation; //导入方法依赖的package包/类
public double getMetricScore(Evaluation eval, PerformanceMetric metric) {
    if (metric.getName().equals("accuracy")) {
        return eval.pctCorrect();
    } else if (metric.getName().equals("auc")) {
        return eval.areaUnderROC(0);
    } else if (metric.getName().equals("rmse")) {
        return eval.rootMeanSquaredError();
    } else if (metric.getName().equals("mae")) {
        return eval.meanAbsoluteError();
    } else if (metric.getName().equals("logLoss")) {
        return eval.SFMeanSchemeEntropy();
    } else if (metric.getName().equals("rmsle")) {
        return eval.rootMeanSquaredLogError();
    }
    throw new RuntimeException(this.getClass().getName() + "impl me please: " + metric.getName());
}
 
开发者ID:williamClanton,项目名称:jbossBA,代码行数:17,代码来源:WekaApacheEngine.java

示例2: evaluate

import weka.classifiers.Evaluation; //导入方法依赖的package包/类
public static double[] evaluate(Classifier model) throws Exception {

		double results[] = new double[4];

		String[] labelFiles = new String[] { "churn", "appetency", "upselling" };

		double overallScore = 0.0;
		for (int i = 0; i < labelFiles.length; i++) {

			// Load data
			Instances train_data = loadData("data/orange_small_train.data",
											"data/orange_small_train_" + labelFiles[i]+ ".labels.txt");
			train_data = preProcessData(train_data);

			// cross-validate the data
			Evaluation eval = new Evaluation(train_data);
			eval.crossValidateModel(model, train_data, 5, new Random(1), new Object[] {});

			// Save results
			results[i] = eval.areaUnderROC(train_data.classAttribute()
					.indexOfValue("1"));
			overallScore += results[i];
			System.out.println(labelFiles[i] + "\t-->\t" +results[i]);
		}
		// Get average results over all three problems
		results[3] = overallScore / 3;
		return results;
	}
 
开发者ID:PacktPublishing,项目名称:Machine-Learning-End-to-Endguide-for-Java-developers,代码行数:29,代码来源:KddCup.java

示例3: computeSingleRunResults

import weka.classifiers.Evaluation; //导入方法依赖的package包/类
public void computeSingleRunResults(MethodEvaluation methodEvaluation)
{
    Evaluation evaluation = methodEvaluation.getEvaluation();
    int hamIndex = HAM.ordinal();
    int spamIndex = SPAM.ordinal();

    Double hamPrecision = 100.0 * evaluation.precision(hamIndex);
    Double spamPrecision = 100.0 * evaluation.precision(spamIndex);
    Double weightedPrecision = 100.0 * evaluation.weightedPrecision();

    Double hamRecall = 100.0 * evaluation.recall(hamIndex);
    Double spamRecall = 100.0 * evaluation.recall(spamIndex);
    Double weightedRecall = 100.0 * evaluation.weightedRecall();

    Double hamAreaUnderPRC = 100.0 * evaluation.areaUnderPRC(hamIndex);
    Double spamAreaUnderPRC = 100.0 * evaluation.areaUnderPRC(spamIndex);
    Double weightedAreaUnderPRC = 100.0 * evaluation.weightedAreaUnderPRC();

    Double hamAreaUnderROC = 100.0 * evaluation.areaUnderROC(hamIndex);
    Double spamAreaUnderROC = 100.0 * evaluation.areaUnderROC(spamIndex);
    Double weightedAreaUnderROC = 100.0 * evaluation.weightedAreaUnderROC();

    Double hamFMeasure = 100.0 * evaluation.fMeasure(hamIndex);
    Double spamFMeasure = 100.0 * evaluation.fMeasure(spamIndex);
    Double weightedFMeasure = 100.0 * evaluation.weightedFMeasure();

    Double trainTime = (double) (methodEvaluation.getTrainEnd() - methodEvaluation.getTrainStart());

    Double testTime = (double) (methodEvaluation.getTestEnd() - methodEvaluation.getTestStart());

    addSingleRunResult(Metric.HAM_PRECISION, hamPrecision);
    addSingleRunResult(Metric.SPAM_PRECISION, spamPrecision);
    addSingleRunResult(Metric.WEIGHTED_PRECISION, weightedPrecision);
    addSingleRunResult(Metric.HAM_RECALL, hamRecall);
    addSingleRunResult(Metric.SPAM_RECALL, spamRecall);
    addSingleRunResult(Metric.WEIGHTED_RECALL, weightedRecall);
    addSingleRunResult(Metric.HAM_AREA_UNDER_PRC, hamAreaUnderPRC);
    addSingleRunResult(Metric.SPAM_AREA_UNDER_PRC, spamAreaUnderPRC);
    addSingleRunResult(Metric.WEIGHTED_AREA_UNDER_PRC, weightedAreaUnderPRC);
    addSingleRunResult(Metric.HAM_AREA_UNDER_ROC, hamAreaUnderROC);
    addSingleRunResult(Metric.SPAM_AREA_UNDER_ROC, spamAreaUnderROC);
    addSingleRunResult(Metric.WEIGHTED_AREA_UNDER_ROC, weightedAreaUnderROC);
    addSingleRunResult(Metric.HAM_F_MEASURE, hamFMeasure);
    addSingleRunResult(Metric.SPAM_F_MEASURE, spamFMeasure);
    addSingleRunResult(Metric.WEIGHTED_F_MEASURE, weightedFMeasure);
    addSingleRunResult(Metric.TRAIN_TIME, trainTime);
    addSingleRunResult(Metric.TEST_TIME, testTime);
}
 
开发者ID:marcelovca90,项目名称:anti-spam-weka-gui,代码行数:49,代码来源:ExperimentHelper.java

示例4: getScore

import weka.classifiers.Evaluation; //导入方法依赖的package包/类
public float getScore(Evaluation eval, Instances testingData) {
    return (float)(1.0 - eval.areaUnderROC(testingData.classIndex())); 
}
 
开发者ID:dsibournemouth,项目名称:autoweka,代码行数:4,代码来源:ClassifierResult.java


注:本文中的weka.classifiers.Evaluation.areaUnderROC方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。