// Test the classification result of each map that a user played, // with the data available as if they were playing through it public static void classifyTest(String dataString, String playerID) { String results = ""; try { java.io.StringReader stringReader = new java.io.StringReader(dataString); java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader); /* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/ //weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff"); weka.core.Instances data = new weka.core.Instances(buffReader); //source.getDataSet(); // setting class attribute if the data format does not provide this information // For example, the XRFF format saves the class attribute information as well if (data.classIndex() == -1) { data.setClassIndex(data.numAttributes() - 1); } weka.classifiers.Classifier cl; for (int i = 3; i < data.numInstances(); i++) { cl = new weka.classifiers.bayes.NaiveBayes(); //cl = new weka.classifiers.trees.J48(); //cl = new weka.classifiers.lazy.IB1(); //cl = new weka.classifiers.functions.MultilayerPerceptron(); ((weka.classifiers.functions.MultilayerPerceptron)cl).setHiddenLayers("12"); weka.core.Instances subset = new weka.core.Instances(data, 0, i); cl.buildClassifier(subset); weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(subset); eval.crossValidateModel(cl, subset, 3, new java.util.Random(1)); results = results + eval.pctCorrect(); // For accuracy measurement /* For Mathews Correlation Coefficient */ //double TP = eval.numTruePositives(1); //double FP = eval.numFalsePositives(1); //double TN = eval.numTrueNegatives(1); //double FN = eval.numFalseNegatives(1); //double correlationCoeff = ((TP*TN)-(FP*FN))/Math.Sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)); //results = results + correlationCoeff; if (i != data.numInstances() - 1) { results = results + ", "; } if (i == data.numInstances() - 1) { Debug.Log("Player: " + playerID + ", Num Maps: " + data.numInstances() + ", AUC: " + eval.areaUnderROC(1)); } } } catch (java.lang.Exception ex) { Debug.LogError(ex.getMessage()); } // Write values to file for a matlab read // For accuracy StreamWriter writer = new StreamWriter("DataForMatlab/" + playerID + "_CrossFoldValidations_NeuralNet.txt"); //StreamWriter writer = new StreamWriter("DataForMatlab/"+playerID+"_CrossFoldCorrCoeff.txt"); // For mathews cc writer.WriteLine(results); writer.Close(); Debug.Log(playerID + " has been written to file"); }
public static void Test() { weka.core.Instances data = new weka.core.Instances(new java.io.FileReader("./data/Classification/Communication.arff")); data.setClassIndex(data.numAttributes() - 1); weka.classifiers.Classifier cls = new weka.classifiers.bayes.BayesNet(); //Save BayesNet results in .txt file using (System.IO.StreamWriter file = new System.IO.StreamWriter("./data/Classification/Communication_Report.txt")) { file.WriteLine("Performing " + percentSplit + "% split evaluation."); int runs = 1; // perform cross-validation for (int i = 0; i < runs; i++) { // randomize data int seed = i + 1; java.util.Random rand = new java.util.Random(seed); weka.core.Instances randData = new weka.core.Instances(data); randData.randomize(rand); //weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData); int trainSize = (int)Math.Round((double)data.numInstances() * percentSplit / 100); int testSize = data.numInstances() - trainSize; weka.core.Instances train = new weka.core.Instances(data, 0, 0); weka.core.Instances test = new weka.core.Instances(data, 0, 0); train.setClassIndex(train.numAttributes() - 1); test.setClassIndex(test.numAttributes() - 1); //Print classifier analytics for all the dataset file.WriteLine("EVALUATION OF TEST DATASET."); //int numCorrect = 0; for (int j = 0; j < data.numInstances(); j++) { weka.core.Instance currentInst = randData.instance(j); if (j < trainSize) { train.add(currentInst); } else { test.add(currentInst); /* * double predictedClass = cls.classifyInstance(currentInst); * * double[] prediction = cls.distributionForInstance(currentInst); * * for (int p = 0; p < prediction.Length; p++) * { * file.WriteLine("Probability of class [{0}] for [{1}] is: {2}", currentInst.classAttribute().value(p), currentInst, Math.Round(prediction[p], 4)); * } * file.WriteLine(); * * file.WriteLine(); * if (predictedClass == data.instance(j).classValue()) * numCorrect++;*/ } } // build and evaluate classifier cls.buildClassifier(train); // Test the model weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData); eval.evaluateModel(cls, test); // Print the results as in Weka explorer: //Print statistics String strSummaryTest = eval.toSummaryString(); file.WriteLine(strSummaryTest); file.WriteLine(); //Print detailed class statistics file.WriteLine(eval.toClassDetailsString()); file.WriteLine(); //Print confusion matrix file.WriteLine(eval.toMatrixString()); file.WriteLine(); // Get the confusion matrix double[][] cmMatrixTest = eval.confusionMatrix(); System.Console.WriteLine("Bayesian Network results saved in Communication_Report.txt file successfully."); } } }
public static void BayesTest() { try { weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("iris.arff")); insts.setClassIndex(insts.numAttributes() - 1); weka.classifiers.Classifier cl = new weka.classifiers.bayes.BayesNet(); System.Console.WriteLine("Performing " + percentSplit + "% split evaluation."); //randomize the order of the instances in the dataset. weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize(); myRandom.setInputFormat(insts); insts = weka.filters.Filter.useFilter(insts, myRandom); int trainSize = insts.numInstances() * percentSplit / 100; int testSize = insts.numInstances() - trainSize; weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize); weka.core.Instances test = new weka.core.Instances(insts, 0, 0); cl.buildClassifier(train); //print model System.Console.WriteLine(cl); int numCorrect = 0; for (int i = trainSize; i < insts.numInstances(); i++) { weka.core.Instance currentInst = insts.instance(i); double predictedClass = cl.classifyInstance(currentInst); test.add(currentInst); double[] prediction = cl.distributionForInstance(currentInst); for (int x = 0; x < prediction.Length; x++) { System.Console.WriteLine("Probability of class [{0}] for [{1}] is: {2}", currentInst.classAttribute().value(x), currentInst, Math.Round(prediction[x], 4)); } System.Console.WriteLine(); if (predictedClass == insts.instance(i).classValue()) { numCorrect++; } } System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" + (double)((double)numCorrect / (double)testSize * 100.0) + "%)"); // Train the model weka.classifiers.Evaluation eTrain = new weka.classifiers.Evaluation(train); eTrain.evaluateModel(cl, train); // Print the results as in Weka explorer: //Print statistics String strSummaryTrain = eTrain.toSummaryString(); System.Console.WriteLine(strSummaryTrain); //Print detailed class statistics System.Console.WriteLine(eTrain.toClassDetailsString()); //Print confusion matrix System.Console.WriteLine(eTrain.toMatrixString()); // Get the confusion matrix double[][] cmMatrixTrain = eTrain.confusionMatrix(); // Test the model weka.classifiers.Evaluation eTest = new weka.classifiers.Evaluation(test); eTest.evaluateModel(cl, test); // Print the results as in Weka explorer: //Print statistics String strSummaryTest = eTest.toSummaryString(); System.Console.WriteLine(strSummaryTest); //Print detailed class statistics System.Console.WriteLine(eTest.toClassDetailsString()); //Print confusion matrix System.Console.WriteLine(eTest.toMatrixString()); // Get the confusion matrix double[][] cmMatrixTest = eTest.confusionMatrix(); } catch (java.lang.Exception ex) { ex.printStackTrace(); } }
public static void cvdTest() { weka.core.Instances data = new weka.core.Instances(new java.io.FileReader("./data/Classification/Communication.arff")); data.setClassIndex(data.numAttributes() - 1); weka.classifiers.Classifier cls = new weka.classifiers.bayes.NaiveBayes(); //Save BayesNet results in .txt file using (System.IO.StreamWriter file = new System.IO.StreamWriter("./data/Classification/Communication_Report.txt")) { int runs = 1; int folds = 10; // perform cross-validation for (int i = 0; i < runs; i++) { // randomize data int seed = i + 1; java.util.Random rand = new java.util.Random(seed); weka.core.Instances randData = new weka.core.Instances(data); randData.randomize(rand); if (randData.classAttribute().isNominal()) { randData.stratify(folds); } weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData); for (int n = 0; n < folds; n++) { weka.core.Instances train = randData.trainCV(folds, n); weka.core.Instances test = randData.testCV(folds, n); // build and evaluate classifier //weka.classifiers.Classifier clsCopy = weka.classifiers.Classifier.makeCopy(cls); cls.buildClassifier(train); //eval.evaluateModel(cls, test); //Print classifier analytics for all the dataset file.WriteLine("EVALUATION OF TEST DATASET."); // Test the model weka.classifiers.Evaluation eTest = new weka.classifiers.Evaluation(test); eTest.evaluateModel(cls, test); // Print the results as in Weka explorer: //Print statistics String strSummaryTest = eTest.toSummaryString(); file.WriteLine(strSummaryTest); file.WriteLine(); //Print detailed class statistics file.WriteLine(eTest.toClassDetailsString()); file.WriteLine(); //Print confusion matrix file.WriteLine(eTest.toMatrixString()); file.WriteLine(); // Get the confusion matrix double[][] cmMatrixTest = eTest.confusionMatrix(); System.Console.WriteLine("Bayesian Network results saved in Communication_Report.txt file successfully."); } //Print classifier analytics for all the dataset file.WriteLine("EVALUATION OF ALL DATASET."); cls.buildClassifier(data); // Train the model weka.classifiers.Evaluation eAlldata = new weka.classifiers.Evaluation(data); eAlldata.evaluateModel(cls, data); // Print the results as in Weka explorer: //Print statistics String strSummaryAlldata = eAlldata.toSummaryString(); file.WriteLine(strSummaryAlldata); file.WriteLine(); //Print detailed class statistics file.WriteLine(eAlldata.toClassDetailsString()); file.WriteLine(); //Print confusion matrix file.WriteLine(eAlldata.toMatrixString()); file.WriteLine("----------------"); //print model file.WriteLine(cls); file.WriteLine(); } } }
private void buttonNewClassificationProcess_Click(object sender, EventArgs e) { cMachineLearning MachineLearningForClassif = new cMachineLearning(this.GlobalInfo); //cParamAlgo AlgoAndParameters = MachineLearningForClassif.AskAndGetClassifAlgo(); // List<bool> ListClassSelected = ((PanelForClassSelection)panelForClassSelectionClustering.Controls[0]).GetListSelectedClass(); // cInfoClass InfoClass = new cInfoClass(); //Instances ListInstances = CompleteScreening.GetCurrentDisplayPlate().CreateInstancesWithClasses(ListClassSelected); cInfoClass InfoClass = cGlobalInfo.CurrentScreening.GetCurrentDisplayPlate().GetNumberOfClassesBut(comboBoxNeutralClassForClassif.SelectedIndex); Instances ListInstances = cGlobalInfo.CurrentScreening.GetCurrentDisplayPlate().CreateInstancesWithClasses(InfoClass, comboBoxNeutralClassForClassif.SelectedIndex); //panelForClassSelectionClustering weka.classifiers.Evaluation EvalClassif = new weka.classifiers.Evaluation(ListInstances); MachineLearningForClassif.PerformTraining(MachineLearningForClassif.AskAndGetClassifAlgo(), ListInstances, //InfoClass.NumberOfClass, richTextBoxInfoClustering, panelTMPForFeedBack, out EvalClassif, false); }
private static void WekaTrainingPipeline(Instances data) { //grab SMO, config TrainingTesting_SharedVariables.smo = new SMO(); TrainingTesting_SharedVariables.smo.setOptions(weka.core.Utils.splitOptions(" -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\"")); GuiPreferences.Instance.setLog("SMO Assigned."); //train TrainingTesting_SharedVariables.smo.buildClassifier(data); GuiPreferences.Instance.setLog("Training on Data"); //test on self should get 100% weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data); eval.evaluateModel(TrainingTesting_SharedVariables.smo, data); Training_Output.printWekaResults(eval.toSummaryString("\nResults\n======\n", false)); GuiPreferences.Instance.setLog("SMO Model Tested on Training Data, check that you get 100%."); //save model serialize model weka.core.SerializationHelper.write(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG.libsvm.arff.model", TrainingTesting_SharedVariables.smo); GuiPreferences.Instance.setLog("SMO Model Serialized and saved."); //load model deserialize model TrainingTesting_SharedVariables.smo = (weka.classifiers.functions.SMO)weka.core.SerializationHelper.read(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG.libsvm.arff.model"); GuiPreferences.Instance.setLog("SMO Model DeSerialized and loaded."); //test loaded model eval = new weka.classifiers.Evaluation(data); eval.evaluateModel(TrainingTesting_SharedVariables.smo, data); Training_Output.printWekaResults(eval.toSummaryString("\nResults\n======\n", false)); GuiPreferences.Instance.setLog("SMO Model Tested on data (sanity check for loaded model)."); //display top IG on dicom view if (Preferences.Instance.attsel == null) { GuiPreferences.Instance.setLog("there are no ranked IG attributes or selected attr, continuing but please fix this possible bug."); } GuiPreferences.Instance.setLog("Dicom Viewer Displaying.."); string dicomDir = GuiPreferences.Instance.WorkDirectory; dicomDir = dicomDir.Substring(0, dicomDir.Length - 4) + @"master\"; string[] files = System.IO.Directory.GetFiles(dicomDir, "*.dcm"); string firstFile = files[0].Substring(files[0].LastIndexOf(@"\") + 1); bool thresholdOrVoxelAmount; if (GuiPreferences.Instance.IgSelectionType == IGType.Threshold) { thresholdOrVoxelAmount = true; } else { thresholdOrVoxelAmount = false; } Form plotForm = new DicomImageViewer.MainForm(dicomDir + firstFile, firstFile, Preferences.Instance.attsel.rankedAttributes(), Convert.ToDouble(GuiPreferences.Instance.NudIGThreshold), Convert.ToInt32(GuiPreferences.Instance.NudIGVoxelAmount), thresholdOrVoxelAmount);// _trainTopIGFeatures); plotForm.StartPosition = FormStartPosition.CenterParent; plotForm.ShowDialog(); plotForm.Close(); GuiPreferences.Instance.setLog("Dicom Viewer Closed."); }