public static void main(String[] args) throws Exception {
BufferedReader reader = new BufferedReader(new FileReader("spambase.arff"));
Instances data = new Instances(reader);
reader.close();
// setting class attribute
data.setClassIndex(data.numAttributes() - 1);
int i = data.numInstances();
int j = data.numAttributes() - 1;
File file = new File("tablelog.csv");
Writer output = null;
output = new BufferedWriter(new FileWriter(file));
output.write(
"%missing,auc1,correct1,fmeasure1,auc2,correct2,fmeasure2,auc3,correct3,fmeasure3\n");
Random randomGenerator = new Random();
data.randomize(randomGenerator);
int numBlock = data.numInstances() / 2;
double num0 = 0, num1 = 0, num2 = 0;
/*mdata.instance(0).setMissing(0);
mdata.deleteWithMissing(0);
System.out.println(mdata.numInstances()+","+data.numInstances());*/
// Instances traindata=null;
// Instances testdata=null;
// System.out.println(data.instance(3).stringValue(1));
for (int perc = 10; perc < 101; perc = perc + 10) {
Instances mdata = new Instances(data);
int numMissing = perc * numBlock / 100;
double y11[] = new double[2];
double y21[] = new double[2];
double y31[] = new double[2];
double y12[] = new double[2];
double y22[] = new double[2];
double y32[] = new double[2];
double y13[] = new double[2];
double y23[] = new double[2];
double y33[] = new double[2];
for (int p = 0; p < 2; p++) {
Instances traindata = mdata.trainCV(2, p);
Instances testdata = mdata.testCV(2, p);
num0 = 0;
num1 = 0;
num2 = 0;
for (int t = 0; t < numBlock; t++) {
if (traindata.instance(t).classValue() == 0) num0++;
if (traindata.instance(t).classValue() == 1) num1++;
// if (traindata.instance(t).classValue()==2) num2++;
}
// System.out.println(mdata.instance(0).classValue());
Instances trainwithmissing = new Instances(traindata);
Instances testwithmissing = new Instances(testdata);
for (int q = 0; q < j; q++) {
int r = randomGenerator.nextInt((int) i / 2);
for (int k = 0; k < numMissing; k++) {
// int r = randomGenerator.nextInt((int) i/2);
// int c = randomGenerator.nextInt(j);
trainwithmissing.instance((r + k) % numBlock).setMissing(q);
testwithmissing.instance((r + k) % numBlock).setMissing(q);
}
}
// trainwithmissing.deleteWithMissing(0);System.out.println(traindata.numInstances()+","+trainwithmissing.numInstances());
Classifier cModel =
(Classifier) new Logistic(); // try for different classifiers and datasets
cModel.buildClassifier(trainwithmissing);
Evaluation eTest1 = new Evaluation(trainwithmissing);
eTest1.evaluateModel(cModel, testdata);
// eTest.crossValidateModel(cModel,mdata,10,mdata.getRandomNumberGenerator(1));
y11[p] =
num0 / numBlock * eTest1.areaUnderROC(0)
+ num1
/ numBlock
* eTest1.areaUnderROC(1) /*+num2/numBlock*eTest1.areaUnderROC(2)*/;
y21[p] = eTest1.correct();
y31[p] =
num0 / numBlock * eTest1.fMeasure(0)
+ num1 / numBlock * eTest1.fMeasure(1) /*+num2/numBlock*eTest1.fMeasure(2)*/;
Classifier cModel2 = (Classifier) new Logistic();
cModel2.buildClassifier(traindata);
Evaluation eTest2 = new Evaluation(traindata);
eTest2.evaluateModel(cModel2, testwithmissing);
y12[p] =
num0 / numBlock * eTest2.areaUnderROC(0)
+ num1
/ numBlock
* eTest2.areaUnderROC(1) /*+num2/numBlock*eTest2.areaUnderROC(2)*/;
y22[p] = eTest2.correct();
y32[p] =
num0 / numBlock * eTest2.fMeasure(0)
+ num1 / numBlock * eTest2.fMeasure(1) /*+num2/numBlock*eTest2.fMeasure(2)*/;
Classifier cModel3 = (Classifier) new Logistic();
cModel3.buildClassifier(trainwithmissing);
Evaluation eTest3 = new Evaluation(trainwithmissing);
eTest3.evaluateModel(cModel3, testwithmissing);
y13[p] =
num0 / numBlock * eTest3.areaUnderROC(0)
+ num1
/ numBlock
* eTest3.areaUnderROC(1) /*+num2/numBlock*eTest3.areaUnderROC(2)*/;
y23[p] = eTest3.correct();
y33[p] =
num0 / numBlock * eTest3.fMeasure(0)
+ num1 / numBlock * eTest3.fMeasure(1) /*+num2/numBlock*eTest3.fMeasure(2)*/;
// System.out.println(num0+","+num1+","+num2+"\n");
}
double auc1 = (y11[0] + y11[1]) / 2;
double auc2 = (y12[0] + y12[1]) / 2;
double auc3 = (y13[0] + y13[1]) / 2;
double corr1 = (y21[0] + y21[1]) / i;
double corr2 = (y22[0] + y22[1]) / i;
double corr3 = (y23[0] + y23[1]) / i;
double fm1 = (y31[0] + y31[1]) / 2;
double fm2 = (y32[0] + y32[1]) / 2;
double fm3 = (y33[0] + y33[1]) / 2;
output.write(
perc + "," + auc1 + "," + corr1 + "," + fm1 + "," + auc2 + "," + corr2 + "," + fm2 + ","
+ auc3 + "," + corr3 + "," + fm3 + "\n"); // System.out.println(num0);
// mdata=data;
}
output.close();
}
/**
* Evaluates a feature subset by cross validation
*
* @param feature_set the subset to be evaluated
* @param num_atts the number of attributes in the subset
* @return the estimated accuracy
* @throws Exception if subset can't be evaluated
*/
protected double estimatePerformance(BitSet feature_set, int num_atts) throws Exception {
m_evaluation = new Evaluation(m_theInstances);
int i;
int[] fs = new int[num_atts];
double[] instA = new double[num_atts];
int classI = m_theInstances.classIndex();
int index = 0;
for (i = 0; i < m_numAttributes; i++) {
if (feature_set.get(i)) {
fs[index++] = i;
}
}
// create new hash table
m_entries = new Hashtable((int) (m_theInstances.numInstances() * 1.5));
// insert instances into the hash table
for (i = 0; i < m_numInstances; i++) {
Instance inst = m_theInstances.instance(i);
for (int j = 0; j < fs.length; j++) {
if (fs[j] == classI) {
instA[j] = Double.MAX_VALUE; // missing for the class
} else if (inst.isMissing(fs[j])) {
instA[j] = Double.MAX_VALUE;
} else {
instA[j] = inst.value(fs[j]);
}
}
insertIntoTable(inst, instA);
}
if (m_CVFolds == 1) {
// calculate leave one out error
for (i = 0; i < m_numInstances; i++) {
Instance inst = m_theInstances.instance(i);
for (int j = 0; j < fs.length; j++) {
if (fs[j] == classI) {
instA[j] = Double.MAX_VALUE; // missing for the class
} else if (inst.isMissing(fs[j])) {
instA[j] = Double.MAX_VALUE;
} else {
instA[j] = inst.value(fs[j]);
}
}
evaluateInstanceLeaveOneOut(inst, instA);
}
} else {
m_theInstances.randomize(m_rr);
m_theInstances.stratify(m_CVFolds);
// calculate 10 fold cross validation error
for (i = 0; i < m_CVFolds; i++) {
Instances insts = m_theInstances.testCV(m_CVFolds, i);
evaluateFoldCV(insts, fs);
}
}
switch (m_evaluationMeasure) {
case EVAL_DEFAULT:
if (m_classIsNominal) {
return m_evaluation.pctCorrect();
}
return -m_evaluation.rootMeanSquaredError();
case EVAL_ACCURACY:
return m_evaluation.pctCorrect();
case EVAL_RMSE:
return -m_evaluation.rootMeanSquaredError();
case EVAL_MAE:
return -m_evaluation.meanAbsoluteError();
case EVAL_AUC:
double[] classPriors = m_evaluation.getClassPriors();
Utils.normalize(classPriors);
double weightedAUC = 0;
for (i = 0; i < m_theInstances.classAttribute().numValues(); i++) {
double tempAUC = m_evaluation.areaUnderROC(i);
if (!Utils.isMissingValue(tempAUC)) {
weightedAUC += (classPriors[i] * tempAUC);
} else {
System.err.println("Undefined AUC!!");
}
}
return weightedAUC;
}
// shouldn't get here
return 0.0;
}
