Generating cross-validation folds (Java approach)

文獻：

http://weka.wikispaces.com/Generating+cross-validation+folds+%28Java+approach%29

This article describes how to generate train/test splits for?cross-validation?using the Weka API directly.?

The following variables are given:

?Instances data =? ...;?? // contains the full dataset we wann create train/test sets from

?int seed = ...;????????? // the seed for randomizing the data

?int folds = ...;???????? // the number of folds to generate, >=2

?Randomize the data

First, randomize your data:

?Random rand = new Random(seed);?? // create seeded number generator

?randData = new Instances(data); ??// create copy of original data

?randData.randomize(rand);???????? // randomize data with number generator

In case your data has a nominal class and you wanna perform stratified cross-validation:

?randData.stratify(folds);

?Generate the folds

?Single run

Next thing that we have to do is creating the train and the test set:

?for (int n = 0; n < folds; n++) {

?? Instances train = randData.trainCV(folds, n);

?? Instances test = randData.testCV(folds, n);

?

?? // further processing, classification, etc.

?? ...

?}

Note:

• the above code is used by the?weka.filters.supervised.instance.StratifiedRemoveFolds?filter
• the?weka.classifiers.Evaluation?class and the Explorer/Experimenter would use this method for obtaining the train set:

?Instances train = randData.trainCV(folds, n, rand);

?Multiple runs

The example above only performs one run of a cross-validation. In case you want to run 10 runs of 10-fold cross-validation, use the following loop:

?Instances data = ...;? // our dataset again, obtained from somewhere

?int runs = 10;

?for (int i = 0; i < runs; i++) {

?? seed = i+1;? // every run gets a new, but defined seed value

?

?? // see: randomize the data

?? ...

?

?? // see: generate the folds

?? ...

?}

?

?

一個簡單的小實驗：

繼續對上一節中的紅酒和白酒進行分類。分類器沒有變化，只是增加了重復試驗過程

package assignment2;import weka.core.Instances;import weka.core.converters.ConverterUtils.DataSource;import weka.core.Utils;import weka.classifiers.Classifier;import weka.classifiers.Evaluation;import weka.classifiers.trees.J48;import weka.filters.Filter;import weka.filters.unsupervised.attribute.Remove;import java.io.FileReader;import java.util.Random;public class cv_rw {public static Instances getFileInstances(String filename) throws Exception{FileReader frData =new FileReader(filename);Instances data = new Instances(frData);int length= data.numAttributes();String[] options = new String[2];options[0]="-R";options[1]=Integer.toString(length);Remove remove =new Remove();remove.setOptions(options);remove.setInputFormat(data);Instances newData= Filter.useFilter(data, remove);return newData;}public static void main(String[] args) throws Exception {// loads data and set class index Instances data = getFileInstances("D://Weka_tutorial//WineQuality//RedWhiteWine.arff");// System.out.println(instances); data.setClassIndex(data.numAttributes()-1);// classifier// String[] tmpOptions;// String classname;// tmpOptions = Utils.splitOptions(Utils.getOption("W", args));// classname = tmpOptions[0];// tmpOptions[0] = "";// Classifier cls = (Classifier) Utils.forName(Classifier.class, classname, tmpOptions);//// // other options// int runs = Integer.parseInt(Utils.getOption("r", args));//重復試驗// int folds = Integer.parseInt(Utils.getOption("x", args));int runs=1;int folds=10;J48 j48= new J48();// j48.buildClassifier(instances);// perform cross-validationfor (int i = 0; i < runs; i++) {// randomize dataint seed = i + 1;Random rand = new Random(seed);Instances randData = new Instances(data);randData.randomize(rand);// if (randData.classAttribute().isNominal()) //沒看懂這里什么意思，往高手回復，萬分感謝// randData.stratify(folds); Evaluation eval = new Evaluation(randData);for (int n = 0; n < folds; n++) {Instances train = randData.trainCV(folds, n);Instances test = randData.testCV(folds, n);// the above code is used by the StratifiedRemoveFolds filter, the// code below by the Explorer/Experimenter:// Instances train = randData.trainCV(folds, n, rand);// build and evaluate classifier Classifier j48Copy = Classifier.makeCopy(j48);j48Copy.buildClassifier(train);eval.evaluateModel(j48Copy, test);}// output evaluation System.out.println();System.out.println("=== Setup run " + (i+1) + " ===");System.out.println("Classifier: " + j48.getClass().getName());System.out.println("Dataset: " + data.relationName());System.out.println("Folds: " + folds);System.out.println("Seed: " + seed);System.out.println();System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation run " + (i+1) + "===", false));}}}

?

?

運行程序得到實驗結果：

?

=== Setup run 1 ===

Classifier: weka.classifiers.trees.J48

Dataset: RedWhiteWine-weka.filters.unsupervised.instance.Randomize-S42-weka.filters.unsupervised.instance.Randomize-S42-weka.filters.unsupervised.attribute.Remove-R13

Folds: 10

Seed: 1

?

=== 10-fold Cross-validation run 1===

Correctly Classified Instances? ??????6415?????????????? 98.7379 %

Incorrectly Classified Instances??????? 82??????????????? 1.2621 %

Kappa statistic????????????????????????? 0.9658

Mean absolute error????????????????????? 0.0159

Root mean squared error????????????????? 0.1109

Relative absolute error????????????????? 4.2898 %

Root relative squared error???????????? 25.7448 %

Total Number of Instances???????????? 6497? ? ?

轉載于:https://www.cnblogs.com/7899-89/p/3667330.html

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