java - 使用自己的 Java 代码在 WEKA 中获取风险预测

Question

我已经检查了"Making predictions" WEKA 文档，它包含命令行和 GUI 预测的明确说明。

我想知道如何获得预测值，就像下面我使用Agrawal从GUI获得的预测值一样。我自己的 Java 代码中的数据集 ( weka.datagenerators.classifiers.classification.Agrawal ):

inst#,  actual,     predicted,  error,  prediction
1,      1:0,        2:1,        +,      0.941
2,      1:0,        1:0,        ,       1
3,      1:0,        1:0,        ,       1
4,      1:0,        1:0,        ,       1
5,      1:0,        1:0,        ,       1
6,      1:0,        1:0,        ,       1
7,      1:0,        2:1,        +,      0.941
8,      2:1,        2:1,        ,       0.941
9,      2:1,        2:1,        ,       0.941
10,     2:1,        2:1,        ,       0.941
1,      1:0,        1:0,        ,       1
2,      1:0,        1:0,        ,       1
3,      1:0,        1:0,        ,       1

<小时/>

即使it我也无法复制这个结果说:

Java

If you want to perform the classification within your own code, see the classifying instances section of this article, explaining the Weka API in general.

我去了link它说:

Classifying instances

In case you have an unlabeled dataset that you want to classify with your newly trained classifier, you can use the following code snippet. It loads the file /some/where/unlabeled.arff, uses the previously built classifier tree to label the instances, and saves the labeled data as /some/where/labeled.arff.

这不是我想要的情况，因为我只想对当前数据集建模的 k 倍交叉验证预测。

<小时/>

更新

predictions

public FastVector predictions()

Returns the predictions that have been collected.

Returns:

a reference to the FastVector containing the predictions that have been collected. This should be null if no predictions have been collected.

我找到了 predictions() Evaluation 类型对象的方法并使用代码:

Object[] preds = evaluation.predictions().toArray();
for(Object pred : preds) {
    System.out.println(pred);
}

结果是:

...
NOM: 0.0 0.0 1.0 0.9466666666666667 0.05333333333333334
NOM: 0.0 0.0 1.0 0.8947368421052632 0.10526315789473684
NOM: 0.0 0.0 1.0 0.9934883720930232 0.0065116279069767444
NOM: 0.0 0.0 1.0 0.9466666666666667 0.05333333333333334
NOM: 0.0 0.0 1.0 0.9912575655682583 0.008742434431741762
NOM: 0.0 0.0 1.0 0.9934883720930232 0.0065116279069767444
...

这和上面的一样吗？

Answer 1

经过深入的谷歌搜索(并且因为 documentation provides minimal help )我终于找到了答案。

我希望这个明确的答案将来对其他人有所帮助。

• 对于示例代码，我看到了问题 "How to print out the predicted class after cross-validation in WEKA"我很高兴能够解读这个不完整的答案，其中有些内容很难理解。

  这是我的代码，其工作方式与 GUI 的输出类似

  StringBuffer predictionSB = new StringBuffer();
  Range attributesToShow = null;
  Boolean outputDistributions = new Boolean(true);
  
  PlainText predictionOutput = new PlainText();
  predictionOutput.setBuffer(predictionSB);
  predictionOutput.setOutputDistribution(true);
  
  Evaluation evaluation = new Evaluation(data);
  evaluation.crossValidateModel(j48Model, data, numberOfFolds,
          randomNumber, predictionOutput, attributesToShow,
          outputDistributions);
  

  为了帮助您理解，我们需要实现 StringBuffer将被类型转换在 AbstractOutput 对象以便函数crossValidateModel能认出来。

  使用StringBuffer只会导致java.lang.ClassCastException使用 PlainText 时与问题中的类似没有StringBuffer将显示java.lang.IllegalStateException .

  谢谢ManChon U (Kevin)和他们的问题"How to identify the cross-evaluation result to its corresponding instance in the input data set?"让我了解这意味着什么:

  ... you just need a single addition argument that is a concrete subclass of weka.classifiers.evaluation.output.prediction.AbstractOutput. weka.classifiers.evaluation.output.prediction.PlainText is probably the one you want to use. Source

  和

  ... Try creating a PlainText object, which extends AbstractOutput (called output for example) instance and calling output.setBuffer(forPredictionsPrinting) and passing that in instead of the buffer. Source

  这些实际上只是为了创建一个 PlainText对象，放置一个StringBuffer并使用它通过方法 setOutput(boolean) 调整输出以及其他。

  最后，要获得我们想要的预测，只需使用:

  System.out.println(predictionOutput.getBuffer());
  

  其中predictionOutput是 AbstractOutput 中的一个对象家庭( PlainText 、 CSV 、 XML 等)。

• 此外，evaluation.predictions()的结果与 WEKA GUI 中提供的不同。幸运的是，马克·霍尔在问题"Print out the predict class after cross-validation"中解释了这一点。

  Evaluation.predictions() returns a FastVector containing either NominalPrediction or NumericPrediction objects from the weka.classifiers.evaluation package. Calling Evaluation.crossValidateModel() with the additional AbstractOutput object results in the evaluation object printing the prediction/distribution information from Nominal/NumericPrediction objects to the StringBuffer in the format that you see in the Explorer or from the command line.

引用文献:

• "Print out the predict class after cross-validation"
• "How to identify the cross-evaluation result to its corresponding instance in the input data set?"
• "How to print out the predicted class after cross-validation in WEKA"