public ResultExperimentClassification TestClassification(object a)
{
InfaForExperimentClassification infa = (InfaForExperimentClassification)a;
ResultExperimentClassification result = new ResultExperimentClassification();
result.timeTrain = new Stopwatch();
result.timeTest = new Stopwatch();
result.timeСlassification = new Stopwatch();
RandomForest RF = (RandomForest)SerializationHelper.read(infa.fileNameModel); //Загрузка модели
//RandomForest RF = new RandomForest();
result.timeTest.Start();
result.evaluation = evaluateModel(RF, infa.trainDataSet, infa.testDataSet); //Тестирование
result.timeTest.Stop();
result.timeСlassification.Start();
RF.classifyInstance(infa.testDataSet.instance(0)); //Замер времени классификации 1 экземпляра
result.timeСlassification.Stop();
return(result);
}
public ResultExperimentClassification ExperimentClassification(object a)
{
InfaForExperimentClassification infa = (InfaForExperimentClassification)a;
ResultExperimentClassification result = new ResultExperimentClassification();
result.timeTrain = new Stopwatch();
result.timeTest = new Stopwatch();
result.timeСlassification = new Stopwatch();
RandomForest RF = new RandomForest();
RF.setNumIterations(100); //Количество деревьев
result.timeTrain.Start();
RF.buildClassifier(infa.trainDataSet); //Обучние
result.timeTrain.Stop();
result.timeTest.Start();
result.evaluation = evaluateModel(RF, infa.trainDataSet, infa.testDataSet); //Тестирование
result.timeTest.Stop();
result.timeСlassification.Start();
RF.classifyInstance(infa.testDataSet.instance(0)); //Замер времени классификации 1 экземпляра
result.timeСlassification.Stop();
return(result);
}
private async void button8_Click(object sender, EventArgs e)
{
openFileDialog1.Filter = "model файлы (*.model)|*.model|Все файлы (*.*)|*.*";
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
InfaForExperimentClassification infa = new InfaForExperimentClassification(); //Инфа для второго потока
trainDataSet.setClassIndex(trainDataSet.numAttributes() - 1); //Установка указателя на атрибут с классами
testDataSet.setClassIndex(testDataSet.numAttributes() - 1);
infa.trainDataSet = trainDataSet;
infa.testDataSet = testDataSet;
infa.fileNameModel = openFileDialog1.FileName;
labelStatus.Text = "Проводится тестирование...";
ResultExperimentClassification result = await Task.Factory.StartNew(() => TestClassification(infa));
Evaluation evaluation = result.evaluation;
for (int i = 0; i < 2; i++)
{
string[] row = new string[11];
row[0] = $"{i}";
row[1] = evaluation.precision(i).ToString(); //Точность
row[2] = evaluation.recall(i).ToString(); //Полнота
row[3] = evaluation.fMeasure(i).ToString(); //F-мера //Accuracy
row[4] = (evaluation.pctCorrect() / 100).ToString(); //Достоверноесть
row[5] = evaluation.truePositiveRate(i).ToString(); //TPR
row[6] = evaluation.falsePositiveRate(i).ToString(); //FPR
row[7] = evaluation.areaUnderROC(i).ToString(); //ROC
row[8] = result.timeTrain.ElapsedTicks.ToString(); //Время обучения
row[9] = result.timeTest.ElapsedTicks.ToString(); //Время тестирования
row[10] = result.timeСlassification.ElapsedTicks.ToString(); //Время классификации 1 экзепляра
dataGridView5.Rows.Add(row);
}
labelStatus.Text = "Тестирование прошло успешно.";
}
}
private async void button6_Click(object sender, EventArgs e)
{
InfaForExperimentClassification infa = new InfaForExperimentClassification(); //Инфа для второго потока
Instances experimentdataSet = dataSet;
experimentdataSet.setClassIndex(experimentdataSet.numAttributes() - 1); //Установка указателя на атрибут с классами
java.util.Random rnd = new java.util.Random();
experimentdataSet.randomize(rnd); //Перемешивание исходного набора данных
int trainSize = (int)Math.Round((double)(experimentdataSet.numInstances() * 0.66)); //0.66 - Процент даннхы на обучение
int testSize = experimentdataSet.numInstances() - trainSize;
infa.trainDataSet = new Instances(experimentdataSet, 0, trainSize);
infa.testDataSet = new Instances(experimentdataSet, trainSize, testSize);
labelStatus.Text = "Проводится эксперимент...";
ResultExperimentClassification result = await Task.Factory.StartNew(() => ExperimentClassification(infa));
Evaluation evaluation = result.evaluation;
string[] row = new string[10];
row[0] = evaluation.weightedPrecision().ToString(); //Точность
row[1] = evaluation.weightedRecall().ToString(); //Полнота
row[2] = evaluation.weightedFMeasure().ToString(); //F-мера //Accuracy
row[3] = (evaluation.pctCorrect() / 100).ToString(); //Достоверноесть
row[4] = evaluation.weightedTruePositiveRate().ToString(); //TPR
row[5] = evaluation.weightedFalsePositiveRate().ToString(); //FPR
row[6] = evaluation.weightedAreaUnderROC().ToString(); //ROC
row[7] = result.timeTrain.ElapsedTicks.ToString(); //Время обучения
row[8] = result.timeTest.ElapsedTicks.ToString(); //Время тестирования
row[9] = result.timeСlassification.ElapsedTicks.ToString(); //Время классификации 1 экзепляра
dataGridView5.Rows.Add(row);
labelStatus.Text = "Эксперимент завершён";
}