// 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 classifierTwo(string classifierFileName, string predictionModel)
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances wekaInsts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
wekaInsts.setClassIndex(wekaInsts.numAttributes() - 1);
//Classifier nbTree = (Classifier)SerializationHelper.read(Model) as J48;
Instances testDataSet = new Instances(new BufferedReader(new FileReader(classifierFileName)));
testDataSet.setClassIndex(wekaInsts.numAttributes() - 1);
//testDataSet.setClassIndex(10);
Evaluation evaluation = new Evaluation(testDataSet);
J48 model = new J48();
//Classifier myClassifier = (Classifier)SerializationHelper.read(Model) as NaiveBayes;
//Classifier myClassifier = new NaiveBayes();
for (int i = 0; i < testDataSet.numInstances(); i++)
{
Instance instance = testDataSet.instance(i);
//evaluation.evaluateModelOnceAndRecordPrediction(myClassifier, instance);
//evaluation.evaluateModelOnce(myClassifier, instance);
}
foreach (object o in evaluation.predictions().toArray())
{
NominalPrediction prediction = o as NominalPrediction;
if (prediction != null)
{
double[] distribution = prediction.distribution();
double predicted = prediction.predicted();
for (int i = 0; i < distribution.Length; i++)
{
System.Console.WriteLine(distribution[i]);
}
System.Console.WriteLine(predicted);
}
}
System.Console.WriteLine(evaluation);
System.Console.ReadKey();
}
public static void classifierOne(string classifierFileName, string predictionModel)
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances wekaInsts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
wekaInsts.setClassIndex(wekaInsts.numAttributes() - 1);
Classifier cl = new SMO();
//Classifier cl = new NaiveBayes();
java.util.Random random = new java.util.Random(1);
Evaluation evaluation = new Evaluation(wekaInsts);
evaluation.crossValidateModel(cl, wekaInsts, 10, random);
foreach (object o in evaluation.getMetricsToDisplay().toArray())
{
}
int count = 0;
StringBuilder sb = new StringBuilder();
foreach (object o in evaluation.predictions().toArray())
{
NominalPrediction prediction = o as NominalPrediction;
if (prediction != null)
{
double[] distribution = prediction.distribution();
double predicted = prediction.predicted();
double actual = prediction.actual();
string revision = prediction.getRevision();
double weight = prediction.weight();
double margine = prediction.margin();
//bool equals = prediction.@equals();
string distributions = String.Empty;
for (int i = 0; i < distribution.Length; i++)
{
//System.Console.WriteLine(distribution[i]);
distributions += distribution[i];
}
var predictionLine = String.Format("{0} - {1} - {2} - {3} - {4} - {5}\n", actual, predicted, revision, weight, margine, distributions);
sb.Append(predictionLine);
//System.Console.WriteLine(predicted);
}
count++;
}
File_Helper.WriteToFile(sb, predictionModel + "NbCl.txt");
System.Console.WriteLine(count);
System.Console.ReadKey();
}
public String PrintClassifierTestReport()
{
try {
Debug.Log("Classifier: Number of instances: " + playerData.numInstances());
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(playerData);
eval.crossValidateModel(classifier, playerData, 10, new java.util.Random(1));
Debug.Log(eval.toSummaryString("\nClassifier: Cross Validate Results: \n======\n", false));
return (eval.toSummaryString("\nResults\n======\n", false));
}
catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
return null;
}
/// <summary> Evaluates a classifier with the options given in an array of
/// strings. <p/>
///
/// Valid options are: <p/>
///
/// -t name of training file <br/>
/// Name of the file with the training data. (required) <p/>
///
/// -T name of test file <br/>
/// Name of the file with the test data. If missing a cross-validation
/// is performed. <p/>
///
/// -c class index <br/>
/// Index of the class attribute (1, 2, ...; default: last). <p/>
///
/// -x number of folds <br/>
/// The number of folds for the cross-validation (default: 10). <p/>
///
/// -s random number seed <br/>
/// Random number seed for the cross-validation (default: 1). <p/>
///
/// -m file with cost matrix <br/>
/// The name of a file containing a cost matrix. <p/>
///
/// -l name of model input file <br/>
/// Loads classifier from the given file. <p/>
///
/// -d name of model output file <br/>
/// Saves classifier built from the training data into the given file. <p/>
///
/// -v <br/>
/// Outputs no statistics for the training data. <p/>
///
/// -o <br/>
/// Outputs statistics only, not the classifier. <p/>
///
/// -i <br/>
/// Outputs detailed information-retrieval statistics per class. <p/>
///
/// -k <br/>
/// Outputs information-theoretic statistics. <p/>
///
/// -p <br/>
/// Outputs predictions for test instances (and nothing else). <p/>
///
/// -r <br/>
/// Outputs cumulative margin distribution (and nothing else). <p/>
///
/// -g <br/>
/// Only for classifiers that implement "Graphable." Outputs
/// the graph representation of the classifier (and nothing
/// else). <p/>
///
/// </summary>
/// <param name="classifier">machine learning classifier
/// </param>
/// <param name="options">the array of string containing the options
/// </param>
/// <throws> Exception if model could not be evaluated successfully </throws>
/// <returns> a string describing the results
/// </returns>
public static System.String evaluateModel(Classifier classifier, System.String[] options)
{
Instances train = null, tempTrain, test = null, template = null;
int seed = 1, folds = 10, classIndex = - 1;
System.String trainFileName, testFileName, sourceClass, classIndexString, seedString, foldsString, objectInputFileName, objectOutputFileName, attributeRangeString;
bool noOutput = false, printClassifications = false, trainStatistics = true, printMargins = false, printComplexityStatistics = false, printGraph = false, classStatistics = false, printSource = false;
System.Text.StringBuilder text = new System.Text.StringBuilder();
System.IO.StreamReader trainReader = null, testReader = null;
//UPGRADE_TODO: Class 'java.io.ObjectInputStream' was converted to 'System.IO.BinaryReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectInputStream'"
System.IO.BinaryReader objectInputStream = null;
System.IO.Stream objectStream=null;
CostMatrix costMatrix = null;
System.Text.StringBuilder schemeOptionsText = null;
Range attributesToOutput = null;
long trainTimeStart = 0, trainTimeElapsed = 0, testTimeStart = 0, testTimeElapsed = 0;
Classifier classifierBackup;
try
{
// Get basic options (options the same for all schemes)
classIndexString = Utils.getOption('c', options);
if (classIndexString.Length != 0)
{
if (classIndexString.Equals("first"))
classIndex = 1;
else if (classIndexString.Equals("last"))
classIndex = - 1;
else
classIndex = System.Int32.Parse(classIndexString);
}
trainFileName = Utils.getOption('t', options);
objectInputFileName = Utils.getOption('l', options);
objectOutputFileName = Utils.getOption('d', options);
testFileName = Utils.getOption('T', options);
if (trainFileName.Length == 0)
{
if (objectInputFileName.Length == 0)
{
throw new System.Exception("No training file and no object " + "input file given.");
}
if (testFileName.Length == 0)
{
throw new System.Exception("No training file and no test " + "file given.");
}
}
else if ((objectInputFileName.Length != 0) && ((!(classifier is UpdateableClassifier)) || (testFileName.Length == 0)))
{
throw new System.Exception("Classifier not incremental, or no " + "test file provided: can't " + "use both train and model file.");
}
try
{
if (trainFileName.Length != 0)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
trainReader = new System.IO.StreamReader(new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).CurrentEncoding);
}
if (testFileName.Length != 0)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
testReader = new System.IO.StreamReader(new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).CurrentEncoding);
}
if (objectInputFileName.Length != 0)
{
//UPGRADE_TODO: Constructor 'java.io.FileInputStream.FileInputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioFileInputStreamFileInputStream_javalangString'"
objectStream= new System.IO.FileStream(objectInputFileName, System.IO.FileMode.Open, System.IO.FileAccess.Read);
if (objectInputFileName.EndsWith(".gz"))
{
//UPGRADE_ISSUE: Constructor 'java.util.zip.GZIPInputStream.GZIPInputStream' was not converted. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1000_javautilzipGZIPInputStream'"
objectStream= new ICSharpCode.SharpZipLib.GZip.GZipInputStream(objectStream);
}
//UPGRADE_TODO: Class 'java.io.ObjectInputStream' was converted to 'System.IO.BinaryReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectInputStream'"
objectInputStream = new System.IO.BinaryReader(objectStream);
}
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception("Can't open file " + e.Message + '.');
}
if (testFileName.Length != 0)
{
template = test = new Instances(testReader, 1);
if (classIndex != - 1)
{
test.ClassIndex = classIndex - 1;
}
else
{
test.ClassIndex = test.numAttributes() - 1;
}
if (classIndex > test.numAttributes())
{
throw new System.Exception("Index of class attribute too large.");
}
}
if (trainFileName.Length != 0)
{
if ((classifier is UpdateableClassifier) && (testFileName.Length != 0))
{
train = new Instances(trainReader, 1);
}
else
{
train = new Instances(trainReader);
}
template = train;
if (classIndex != - 1)
{
train.ClassIndex = classIndex - 1;
}
else
{
train.ClassIndex = train.numAttributes() - 1;
}
if ((testFileName.Length != 0) && !test.equalHeaders(train))
{
throw new System.ArgumentException("Train and test file not compatible!");
}
if (classIndex > train.numAttributes())
{
throw new System.Exception("Index of class attribute too large.");
}
}
if (template == null)
{
throw new System.Exception("No actual dataset provided to use as template");
}
seedString = Utils.getOption('s', options);
if (seedString.Length != 0)
{
seed = System.Int32.Parse(seedString);
}
foldsString = Utils.getOption('x', options);
if (foldsString.Length != 0)
{
folds = System.Int32.Parse(foldsString);
}
costMatrix = handleCostOption(Utils.getOption('m', options), template.numClasses());
classStatistics = Utils.getFlag('i', options);
noOutput = Utils.getFlag('o', options);
trainStatistics = !Utils.getFlag('v', options);
printComplexityStatistics = Utils.getFlag('k', options);
printMargins = Utils.getFlag('r', options);
printGraph = Utils.getFlag('g', options);
sourceClass = Utils.getOption('z', options);
printSource = (sourceClass.Length != 0);
// Check -p option
try
{
attributeRangeString = Utils.getOption('p', options);
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception(e.Message + "\nNOTE: the -p option has changed. " + "It now expects a parameter specifying a range of attributes " + "to list with the predictions. Use '-p 0' for none.");
}
if (attributeRangeString.Length != 0)
{
// if no test file given, we cannot print predictions
if (testFileName.Length == 0)
throw new System.Exception("Cannot print predictions ('-p') without test file ('-T')!");
printClassifications = true;
if (!attributeRangeString.Equals("0"))
attributesToOutput = new Range(attributeRangeString);
}
// if no training file given, we don't have any priors
if ((trainFileName.Length == 0) && (printComplexityStatistics))
throw new System.Exception("Cannot print complexity statistics ('-k') without training file ('-t')!");
// If a model file is given, we can't process
// scheme-specific options
if (objectInputFileName.Length != 0)
{
Utils.checkForRemainingOptions(options);
}
else
{
// Set options for classifier
// if (classifier instanceof OptionHandler)
// {
// for (int i = 0; i < options.length; i++)
// {
// if (options[i].length() != 0)
// {
// if (schemeOptionsText == null)
// {
// schemeOptionsText = new StringBuffer();
// }
// if (options[i].indexOf(' ') != -1)
// {
// schemeOptionsText.append('"' + options[i] + "\" ");
// }
// else
// {
// schemeOptionsText.append(options[i] + " ");
// }
// }
// }
// ((OptionHandler)classifier).setOptions(options);
// }
}
Utils.checkForRemainingOptions(options);
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception("\nWeka exception: " + e.Message + makeOptionString(classifier));
}
// Setup up evaluation objects
Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
if (objectInputFileName.Length != 0)
{
testingEvaluation.useNoPriors();
// Load classifier from file
//UPGRADE_WARNING: Method 'java.io.ObjectInputStream.readObject' was converted to 'SupportClass.Deserialize' which may throw an exception. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1101'"
//classifier = (Classifier) SupportClass.Deserialize(objectInputStream);
//FileStream fs = new FileStream("DataFile.dat", FileMode.Open);
try
{
BinaryFormatter formatter = new BinaryFormatter();
// Deserialize the hashtable from the file and
// assign the reference to the local variable.
// addresses = (Hashtable)formatter.Deserialize(fs);
classifier = (Classifier)formatter.Deserialize(objectStream);
}
catch (Exception e)
{
Console.WriteLine("Failed to deserialize. Reason: " + e.Message);
throw;
}
finally
{
objectStream.Close();
//fs.Close();
}
objectInputStream.Close();
}
// backup of fully setup classifier for cross-validation
classifierBackup = Classifier.makeCopy(classifier);
// Build the classifier if no object file provided
if ((classifier is UpdateableClassifier) && (testFileName.Length != 0) && (costMatrix == null) && (trainFileName.Length != 0))
{
// Build classifier incrementally
trainingEvaluation.Priors = train;
testingEvaluation.Priors = train;
trainTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
if (objectInputFileName.Length == 0)
{
classifier.buildClassifier(train);
}
while (train.readInstance(trainReader))
{
trainingEvaluation.updatePriors(train.instance(0));
testingEvaluation.updatePriors(train.instance(0));
((UpdateableClassifier) classifier).updateClassifier(train.instance(0));
train.delete(0);
}
trainTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - trainTimeStart;
trainReader.Close();
}
else if (objectInputFileName.Length == 0)
{
// Build classifier in one go
tempTrain = new Instances(train);
trainingEvaluation.Priors = tempTrain;
testingEvaluation.Priors = tempTrain;
trainTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
classifier.buildClassifier(tempTrain);
trainTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - trainTimeStart;
}
// Save the classifier if an object output file is provided
if (objectOutputFileName.Length != 0)
{
//UPGRADE_TODO: Constructor 'java.io.FileOutputStream.FileOutputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioFileOutputStreamFileOutputStream_javalangString'"
System.IO.Stream os = new System.IO.FileStream(objectOutputFileName, System.IO.FileMode.Create);
if (objectOutputFileName.EndsWith(".gz"))
{
//UPGRADE_ISSUE: Constructor 'java.util.zip.GZIPOutputStream.GZIPOutputStream' was not converted. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1000_javautilzipGZIPOutputStream'"
os = new ICSharpCode.SharpZipLib.GZip.GZipOutputStream(os);
}
//UPGRADE_TODO: Class 'java.io.ObjectOutputStream' was converted to 'System.IO.BinaryWriter' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectOutputStream'"
System.IO.BinaryWriter objectOutputStream = new System.IO.BinaryWriter(os);
//UPGRADE_TODO: Method 'java.io.ObjectOutputStream.writeObject' was converted to 'SupportClass.Serialize' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectOutputStreamwriteObject_javalangObject'"
//SupportClass.Serialize(objectOutputStream, classifier);
BinaryFormatter bformatter = new BinaryFormatter();
bformatter.Serialize(os, classifier);
objectOutputStream.Flush();
objectOutputStream.Close();
}
// If classifier is drawable output string describing graph
if ((classifier is Drawable) && (printGraph))
{
return ((Drawable) classifier).graph();
}
// Output the classifier as equivalent source
if ((classifier is Sourcable) && (printSource))
{
return wekaStaticWrapper((Sourcable) classifier, sourceClass);
}
// Output test instance predictions only
if (printClassifications)
{
return toPrintClassifications(classifier, new Instances(template, 0), testFileName, classIndex, attributesToOutput);
}
// Output model
if (!(noOutput || printMargins))
{
// if (classifier instanceof OptionHandler)
// {
// if (schemeOptionsText != null)
// {
// text.append("\nOptions: "+schemeOptionsText);
// text.append("\n");
// }
// }
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Object.toString' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
text.Append("\n" + classifier.ToString() + "\n");
}
if (!printMargins && (costMatrix != null))
{
text.Append("\n=== Evaluation Cost Matrix ===\n\n").Append(costMatrix.ToString());
}
// Compute error estimate from training data
if ((trainStatistics) && (trainFileName.Length != 0))
{
if ((classifier is UpdateableClassifier) && (testFileName.Length != 0) && (costMatrix == null))
{
// Classifier was trained incrementally, so we have to
// reopen the training data in order to test on it.
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
trainReader = new System.IO.StreamReader(new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).CurrentEncoding);
// Incremental testing
train = new Instances(trainReader, 1);
if (classIndex != - 1)
{
train.ClassIndex = classIndex - 1;
}
else
{
train.ClassIndex = train.numAttributes() - 1;
}
testTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
while (train.readInstance(trainReader))
{
trainingEvaluation.evaluateModelOnce((Classifier) classifier, train.instance(0));
train.delete(0);
}
testTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - testTimeStart;
trainReader.Close();
}
else
{
testTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
trainingEvaluation.evaluateModel(classifier, train);
testTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - testTimeStart;
}
// Print the results of the training evaluation
if (printMargins)
{
return trainingEvaluation.toCumulativeMarginDistributionString();
}
else
{
text.Append("\nTime taken to build model: " + Utils.doubleToString(trainTimeElapsed / 1000.0, 2) + " seconds");
text.Append("\nTime taken to test model on training data: " + Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds");
text.Append(trainingEvaluation.toSummaryString("\n\n=== Error on training" + " data ===\n", printComplexityStatistics));
if (template.classAttribute().Nominal)
{
if (classStatistics)
{
text.Append("\n\n" + trainingEvaluation.toClassDetailsString());
}
text.Append("\n\n" + trainingEvaluation.toMatrixString());
}
}
}
// Compute proper error estimates
if (testFileName.Length != 0)
{
// Testing is on the supplied test data
while (test.readInstance(testReader))
{
testingEvaluation.evaluateModelOnce((Classifier) classifier, test.instance(0));
test.delete(0);
}
testReader.Close();
text.Append("\n\n" + testingEvaluation.toSummaryString("=== Error on test data ===\n", printComplexityStatistics));
}
else if (trainFileName.Length != 0)
{
// Testing is via cross-validation on training data
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.util.Random.Random' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
System.Random random = new System.Random((System.Int32) seed);
// use untrained (!) classifier for cross-validation
classifier = Classifier.makeCopy(classifierBackup);
testingEvaluation.crossValidateModel(classifier, train, folds, random);
if (template.classAttribute().Numeric)
{
text.Append("\n\n\n" + testingEvaluation.toSummaryString("=== Cross-validation ===\n", printComplexityStatistics));
}
else
{
text.Append("\n\n\n" + testingEvaluation.toSummaryString("=== Stratified " + "cross-validation ===\n", printComplexityStatistics));
}
}
if (template.classAttribute().Nominal)
{
if (classStatistics)
{
text.Append("\n\n" + testingEvaluation.toClassDetailsString());
}
text.Append("\n\n" + testingEvaluation.toMatrixString());
}
return text.ToString();
}
/// <summary>
/// Plate by plate classification
/// </summary>
/// <param name="NeutralClass">Neutral class</param>
/// <param name="IdxClassifier">Classifier Index (0:J48), (1:SVM), (2:NN), (3:KNN)</param>
private void ClassificationPlateByPlate(int NeutralClass, int IdxClassifier)
{
int NumberOfPlates = cGlobalInfo.CurrentScreening.ListPlatesActive.Count;
for (int PlateIdx = 0; PlateIdx < NumberOfPlates; PlateIdx++)
{
cPlate CurrentPlateToProcess = cGlobalInfo.CurrentScreening.ListPlatesActive.GetPlate(cGlobalInfo.CurrentScreening.ListPlatesActive[PlateIdx].GetName());
cInfoClass InfoClass = CurrentPlateToProcess.GetNumberOfClassesBut(NeutralClass);
// return;
if (InfoClass.NumberOfClass <= 1)
{
richTextBoxInfoClassif.AppendText(CurrentPlateToProcess.GetName() + " not processed.\n");
continue;
}
weka.core.Instances insts = CurrentPlateToProcess.CreateInstancesWithClasses(InfoClass, NeutralClass);
Classifier ClassificationModel = null;
string Text = "";
switch (IdxClassifier)
{
case 0: // J48
ClassificationModel = new weka.classifiers.trees.J48();
weka.classifiers.trees.J48 J48Model = (weka.classifiers.trees.J48)ClassificationModel;
J48Model.setMinNumObj((int)cGlobalInfo.OptionsWindow.numericUpDownJ48MinNumObjects.Value);
Text = "J48 - ";
break;
case 1: // SVM
ClassificationModel = new weka.classifiers.functions.SMO();
Text = "SVM - ";
break;
case 2: // NN
ClassificationModel = new weka.classifiers.functions.MultilayerPerceptron();
Text = "Neural Network - ";
break;
case 3: // KNN
ClassificationModel = new weka.classifiers.lazy.IBk((int)cGlobalInfo.OptionsWindow.numericUpDownKofKNN.Value);
Text = "K-Nearest Neighbor(s) - ";
break;
case 4: // Random Forest
ClassificationModel = new weka.classifiers.trees.RandomForest();
Text = "Random Forest - ";
break;
default:
break;
}
richTextBoxInfoClassif.AppendText(Text + InfoClass.NumberOfClass + " classes - Plate: ");
richTextBoxInfoClassif.AppendText(CurrentPlateToProcess.GetName() + " OK \n");
weka.core.Instances train = new weka.core.Instances(insts, 0, insts.numInstances());
ClassificationModel.buildClassifier(train);
cGlobalInfo.ConsoleWriteLine(ClassificationModel.ToString());
weka.classifiers.Evaluation evaluation = new weka.classifiers.Evaluation(insts);
evaluation.crossValidateModel(ClassificationModel, insts, 2, new java.util.Random(1));
cGlobalInfo.ConsoleWriteLine(evaluation.toSummaryString());
cGlobalInfo.ConsoleWriteLine(evaluation.toMatrixString());
// update classification information of the current plate
switch (IdxClassifier)
{
case 0: // J48
weka.classifiers.trees.J48 CurrentClassifier = (weka.classifiers.trees.J48)(ClassificationModel);
CurrentPlateToProcess.GetInfoClassif().StringForTree = CurrentClassifier.graph().Remove(0, CurrentClassifier.graph().IndexOf("{") + 2);
break;
/*case 1: // SVM
break;
case 2: // NN
break;
case 3: // KNN
break;*/
default:
break;
}
CurrentPlateToProcess.GetInfoClassif().StringForQuality = evaluation.toSummaryString();
CurrentPlateToProcess.GetInfoClassif().ConfusionMatrix = evaluation.toMatrixString();
foreach (cWell TmpWell in CurrentPlateToProcess.ListActiveWells)
{
weka.core.Instance currentInst = TmpWell.CreateInstanceForNClasses(InfoClass).instance(0);
double predictedClass = ClassificationModel.classifyInstance(currentInst);
TmpWell.SetClass(InfoClass.ListBackAssociation[(int)predictedClass]);
}
}
return;
}
/// <summary>
/// Global classification
/// </summary>
/// <param name="NeutralClass">Neutral class</param>
/// <param name="IdxClassifier">Classifier Index (0:J48), (1:SVM), (2:NN), (3:KNN)</param>
private void ClassificationGlobal(int NeutralClass, int IdxClassifier)
{
cInfoClass InfoClass = cGlobalInfo.CurrentScreening.GetNumberOfClassesBut(NeutralClass);
if (InfoClass.NumberOfClass <= 1)
{
richTextBoxInfoClassif.AppendText("Screening not processed.\n");
return;
}
cExtendedTable TrainingTable = cGlobalInfo.CurrentScreening.ListPlatesActive.GetListActiveWells().GetAverageDescriptorValues(cGlobalInfo.CurrentScreening.ListDescriptors.GetActiveDescriptors(), false, true);
weka.core.Instances insts = cGlobalInfo.CurrentScreening.CreateInstancesWithClasses(InfoClass, NeutralClass);
Classifier ClassificationModel = null;
switch (IdxClassifier)
{
case 0: // J48
ClassificationModel = new weka.classifiers.trees.J48();
weka.classifiers.trees.J48 J48Model = (weka.classifiers.trees.J48)ClassificationModel;
J48Model.setMinNumObj((int)cGlobalInfo.OptionsWindow.numericUpDownJ48MinNumObjects.Value);
richTextBoxInfoClassif.AppendText("\nC4.5 : " + InfoClass.NumberOfClass + " classes");
break;
case 1: // SVM
ClassificationModel = new weka.classifiers.functions.SMO();
break;
case 2: // NN
ClassificationModel = new weka.classifiers.functions.MultilayerPerceptron();
break;
case 3: // KNN
ClassificationModel = new weka.classifiers.lazy.IBk((int)cGlobalInfo.OptionsWindow.numericUpDownKofKNN.Value);
break;
case 4: // Random Forest
ClassificationModel = new weka.classifiers.trees.RandomForest();
break;
default:
break;
}
weka.core.Instances train = new weka.core.Instances(insts, 0, insts.numInstances());
ClassificationModel.buildClassifier(train);
cGlobalInfo.ConsoleWriteLine(ClassificationModel.ToString());
weka.classifiers.Evaluation evaluation = new weka.classifiers.Evaluation(insts);
evaluation.crossValidateModel(ClassificationModel, insts, 2, new java.util.Random(1));
cGlobalInfo.ConsoleWriteLine(evaluation.toSummaryString());
cGlobalInfo.ConsoleWriteLine(evaluation.toMatrixString());
// update classification information of the current plate
string Text = "";
switch (IdxClassifier)
{
case 0: // J48
Text = "J48 - ";
break;
case 1: // SVM
// ClassificationModel = new weka.classifiers.functions.SMO();
Text = "SVM - ";
break;
case 2: // NN
// ClassificationModel = new weka.classifiers.functions.MultilayerPerceptron();
Text = "Neural Network - ";
break;
case 3: // KNN
// ClassificationModel = new weka.classifiers.lazy.IBk((int)CompleteScreening.GlobalInfo.OptionsWindow.numericUpDownKofKNN.Value);
Text = "K-Nearest Neighbor(s) - ";
break;
default:
break;
}
richTextBoxInfoClassif.AppendText(Text + InfoClass.NumberOfClass + " classes.");
// CurrentPlateToProcess.GetInfoClassif().StringForQuality = evaluation.toSummaryString();
// CurrentPlateToProcess.GetInfoClassif().ConfusionMatrix = evaluation.toMatrixString();
foreach (cPlate CurrentPlateToProcess in cGlobalInfo.CurrentScreening.ListPlatesActive)
{
foreach (cWell TmpWell in CurrentPlateToProcess.ListActiveWells)
{
// return;
weka.core.Instance currentInst = TmpWell.CreateInstanceForNClasses(InfoClass).instance(0);
double predictedClass = ClassificationModel.classifyInstance(currentInst);
double[] ClassConfidence = ClassificationModel.distributionForInstance(currentInst);
double ConfidenceValue = ClassConfidence[(int)predictedClass];
TmpWell.SetClass(InfoClass.ListBackAssociation[(int)predictedClass], ConfidenceValue);
}
}
return;
}
/// <summary>
/// Build the learning model for classification
/// </summary>
/// <param name="InstancesList">list of instances </param>
/// <param name="NumberofClusters">Number of Clusters</param>
/// <param name="TextBoxForFeedback">Text box for the results (can be NULL)</param>
/// <param name="PanelForVisualFeedback">Panel to display visual results if avalaible (can be NULL)</param>
public Classifier PerformTraining(FormForClassificationInfo WindowForClassificationParam, Instances InstancesList, /*int NumberofClusters,*/ RichTextBox TextBoxForFeedback,
Panel PanelForVisualFeedback, out weka.classifiers.Evaluation ModelEvaluation, bool IsCellular)
{
// weka.classifiers.Evaluation ModelEvaluation = null;
// FormForClassificationInfo WindowForClassificationParam = new FormForClassificationInfo(GlobalInfo);
ModelEvaluation = null;
// if (WindowForClassificationParam.ShowDialog() != System.Windows.Forms.DialogResult.OK) return null;
// weka.classifiers.Evaluation ModelEvaluation = new Evaluation(
cParamAlgo ClassifAlgoParams = WindowForClassificationParam.GetSelectedAlgoAndParameters();
if (ClassifAlgoParams == null) return null;
//this.Cursor = Cursors.WaitCursor;
// cParamAlgo ClassificationAlgo = WindowForClassificationParam.GetSelectedAlgoAndParameters();
cListValuesParam Parameters = ClassifAlgoParams.GetListValuesParam();
//Classifier this.CurrentClassifier = null;
// -------------------------- Classification -------------------------------
// create the instances
// InstancesList = this.ListInstances;
this.attValsWithoutClasses = new FastVector();
if (IsCellular)
for (int i = 0; i < cGlobalInfo.ListCellularPhenotypes.Count; i++)
this.attValsWithoutClasses.addElement(cGlobalInfo.ListCellularPhenotypes[i].Name);
else
for (int i = 0; i < cGlobalInfo.ListWellClasses.Count; i++)
this.attValsWithoutClasses.addElement(cGlobalInfo.ListWellClasses[i].Name);
InstancesList.insertAttributeAt(new weka.core.Attribute("Class", this.attValsWithoutClasses), InstancesList.numAttributes());
//int A = Classes.Count;
for (int i = 0; i < Classes.Count; i++)
InstancesList.get(i).setValue(InstancesList.numAttributes() - 1, Classes[i]);
InstancesList.setClassIndex(InstancesList.numAttributes() - 1);
weka.core.Instances train = new weka.core.Instances(InstancesList, 0, InstancesList.numInstances());
if (PanelForVisualFeedback != null)
PanelForVisualFeedback.Controls.Clear();
#region List classifiers
#region J48
if (ClassifAlgoParams.Name == "J48")
{
this.CurrentClassifier = new weka.classifiers.trees.J48();
((J48)this.CurrentClassifier).setMinNumObj((int)Parameters.ListDoubleValues.Get("numericUpDownMinInstLeaf").Value);
((J48)this.CurrentClassifier).setConfidenceFactor((float)Parameters.ListDoubleValues.Get("numericUpDownConfFactor").Value);
((J48)this.CurrentClassifier).setNumFolds((int)Parameters.ListDoubleValues.Get("numericUpDownNumFolds").Value);
((J48)this.CurrentClassifier).setUnpruned((bool)Parameters.ListCheckValues.Get("checkBoxUnPruned").Value);
((J48)this.CurrentClassifier).setUseLaplace((bool)Parameters.ListCheckValues.Get("checkBoxLaplacianSmoothing").Value);
((J48)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
((J48)this.CurrentClassifier).setSubtreeRaising((bool)Parameters.ListCheckValues.Get("checkBoxSubTreeRaising").Value);
// CurrentClassif.SetJ48Tree((J48)this.CurrentClassifier, Classes.Length);
this.CurrentClassifier.buildClassifier(train);
// display results training
// display tree
if (PanelForVisualFeedback != null)
{
GViewer GraphView = DisplayTree(GlobalInfo, ((J48)this.CurrentClassifier), IsCellular).gViewerForTreeClassif;
GraphView.Size = new System.Drawing.Size(PanelForVisualFeedback.Width, PanelForVisualFeedback.Height);
GraphView.Anchor = (AnchorStyles.Bottom | AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right);
PanelForVisualFeedback.Controls.Clear();
PanelForVisualFeedback.Controls.Add(GraphView);
}
}
#endregion
#region Random Tree
else if (ClassifAlgoParams.Name == "RandomTree")
{
this.CurrentClassifier = new weka.classifiers.trees.RandomTree();
if ((bool)Parameters.ListCheckValues.Get("checkBoxMaxDepthUnlimited").Value)
((RandomTree)this.CurrentClassifier).setMaxDepth(0);
else
((RandomTree)this.CurrentClassifier).setMaxDepth((int)Parameters.ListDoubleValues.Get("numericUpDownMaxDepth").Value);
((RandomTree)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
((RandomTree)this.CurrentClassifier).setMinNum((double)Parameters.ListDoubleValues.Get("numericUpDownMinWeight").Value);
if ((bool)Parameters.ListCheckValues.Get("checkBoxIsBackfitting").Value)
{
((RandomTree)this.CurrentClassifier).setNumFolds((int)Parameters.ListDoubleValues.Get("numericUpDownBackFittingFolds").Value);
}
else
{
((RandomTree)this.CurrentClassifier).setNumFolds(0);
}
this.CurrentClassifier.buildClassifier(train);
//string StringForTree = ((RandomTree)this.CurrentClassifier).graph().Remove(0, ((RandomTree)this.CurrentClassifier).graph().IndexOf("{") + 2);
//Microsoft.Msagl.GraphViewerGdi.GViewer GraphView = new GViewer();
//GraphView.Graph = GlobalInfo.WindowHCSAnalyzer.ComputeAndDisplayGraph(StringForTree);//.Remove(StringForTree.Length - 3, 3));
//GraphView.Size = new System.Drawing.Size(panelForGraphicalResults.Width, panelForGraphicalResults.Height);
//GraphView.Anchor = (AnchorStyles.Bottom | AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right);
//this.panelForGraphicalResults.Controls.Clear();
//this.panelForGraphicalResults.Controls.Add(GraphView);
}
#endregion
#region Random Forest
else if (ClassifAlgoParams.Name == "RandomForest")
{
this.CurrentClassifier = new weka.classifiers.trees.RandomForest();
if ((bool)Parameters.ListCheckValues.Get("checkBoxMaxDepthUnlimited").Value)
((RandomForest)this.CurrentClassifier).setMaxDepth(0);
else
((RandomForest)this.CurrentClassifier).setMaxDepth((int)Parameters.ListDoubleValues.Get("numericUpDownMaxDepth").Value);
((RandomForest)this.CurrentClassifier).setNumTrees((int)Parameters.ListDoubleValues.Get("numericUpDownNumTrees").Value);
((RandomForest)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region KStar
else if (ClassifAlgoParams.Name == "KStar")
{
this.CurrentClassifier = new weka.classifiers.lazy.KStar();
((KStar)this.CurrentClassifier).setGlobalBlend((int)Parameters.ListDoubleValues.Get("numericUpDownGlobalBlend").Value);
((KStar)this.CurrentClassifier).setEntropicAutoBlend((bool)Parameters.ListCheckValues.Get("checkBoxBlendAuto").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region SVM
else if (ClassifAlgoParams.Name == "SVM")
{
this.CurrentClassifier = new weka.classifiers.functions.SMO();
((SMO)this.CurrentClassifier).setC((double)Parameters.ListDoubleValues.Get("numericUpDownC").Value);
((SMO)this.CurrentClassifier).setKernel(WindowForClassificationParam.GeneratedKernel);
((SMO)this.CurrentClassifier).setRandomSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region KNN
else if (ClassifAlgoParams.Name == "KNN")
{
this.CurrentClassifier = new weka.classifiers.lazy.IBk();
string OptionDistance = " -K " + (int)Parameters.ListDoubleValues.Get("numericUpDownKNN").Value + " -W 0 ";
string WeightType = (string)Parameters.ListTextValues.Get("comboBoxDistanceWeight").Value;
switch (WeightType)
{
case "No Weighting":
OptionDistance += "";
break;
case "1/Distance":
OptionDistance += "-I";
break;
case "1-Distance":
OptionDistance += "-F";
break;
default:
break;
}
OptionDistance += " -A \"weka.core.neighboursearch.LinearNNSearch -A \\\"weka.core.";
string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
// OptionDistance += " -A \"weka.core.";
switch (DistanceType)
{
case "Euclidean":
OptionDistance += "EuclideanDistance";
break;
case "Manhattan":
OptionDistance += "ManhattanDistance";
break;
case "Chebyshev":
OptionDistance += "ChebyshevDistance";
break;
default:
break;
}
if (!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value)
OptionDistance += " -D";
OptionDistance += " -R ";
OptionDistance += "first-last\\\"\"";
((IBk)this.CurrentClassifier).setOptions(weka.core.Utils.splitOptions(OptionDistance));
//((IBk)this.CurrentClassifier).setKNN((int)Parameters.ListDoubleValues.Get("numericUpDownKNN").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region Multilayer Perceptron
else if (ClassifAlgoParams.Name == "Perceptron")
{
this.CurrentClassifier = new weka.classifiers.functions.MultilayerPerceptron();
((MultilayerPerceptron)this.CurrentClassifier).setMomentum((double)Parameters.ListDoubleValues.Get("numericUpDownMomentum").Value);
((MultilayerPerceptron)this.CurrentClassifier).setLearningRate((double)Parameters.ListDoubleValues.Get("numericUpDownLearningRate").Value);
((MultilayerPerceptron)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
((MultilayerPerceptron)this.CurrentClassifier).setTrainingTime((int)Parameters.ListDoubleValues.Get("numericUpDownTrainingTime").Value);
((MultilayerPerceptron)this.CurrentClassifier).setNormalizeAttributes((bool)Parameters.ListCheckValues.Get("checkBoxNormAttribute").Value);
((MultilayerPerceptron)this.CurrentClassifier).setNormalizeNumericClass((bool)Parameters.ListCheckValues.Get("checkBoxNormNumericClasses").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region ZeroR
else if (ClassifAlgoParams.Name == "ZeroR")
{
this.CurrentClassifier = new weka.classifiers.rules.OneR();
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region OneR
else if (ClassifAlgoParams.Name == "OneR")
{
this.CurrentClassifier = new weka.classifiers.rules.OneR();
((OneR)this.CurrentClassifier).setMinBucketSize((int)Parameters.ListDoubleValues.Get("numericUpDownMinBucketSize").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region Naive Bayes
else if (ClassifAlgoParams.Name == "NaiveBayes")
{
this.CurrentClassifier = new weka.classifiers.bayes.NaiveBayes();
((NaiveBayes)this.CurrentClassifier).setUseKernelEstimator((bool)Parameters.ListCheckValues.Get("checkBoxKernelEstimator").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region Logistic
else if (ClassifAlgoParams.Name == "Logistic")
{
this.CurrentClassifier = new weka.classifiers.functions.Logistic();
((Logistic)this.CurrentClassifier).setUseConjugateGradientDescent((bool)Parameters.ListCheckValues.Get("checkBoxUseConjugateGradientDescent").Value);
((Logistic)this.CurrentClassifier).setRidge((double)Parameters.ListDoubleValues.Get("numericUpDownRidge").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
//weka.classifiers.functions.SMO
//BayesNet
#endregion
if (TextBoxForFeedback != null)
{
TextBoxForFeedback.Clear();
TextBoxForFeedback.AppendText(this.CurrentClassifier.ToString());
}
TextBoxForFeedback.AppendText("\n" + (InstancesList.numAttributes() - 1) + " attributes:\n\n");
for (int IdxAttributes = 0; IdxAttributes < InstancesList.numAttributes() - 1; IdxAttributes++)
{
TextBoxForFeedback.AppendText(IdxAttributes + "\t: " + InstancesList.attribute(IdxAttributes).name() + "\n");
}
#region evaluation of the model and results display
if ((WindowForClassificationParam.numericUpDownFoldNumber.Enabled) && (TextBoxForFeedback != null))
{
TextBoxForFeedback.AppendText("\n-----------------------------\nModel validation\n-----------------------------\n");
ModelEvaluation = new weka.classifiers.Evaluation(InstancesList);
ModelEvaluation.crossValidateModel(this.CurrentClassifier, InstancesList, (int)WindowForClassificationParam.numericUpDownFoldNumber.Value, new java.util.Random(1));
TextBoxForFeedback.AppendText(ModelEvaluation.toSummaryString());
TextBoxForFeedback.AppendText("\n-----------------------------\nConfusion Matrix:\n-----------------------------\n");
double[][] ConfusionMatrix = ModelEvaluation.confusionMatrix();
string NewLine = "";
for (int i = 0; i < ConfusionMatrix[0].Length; i++)
{
NewLine += "c" + i + "\t";
}
TextBoxForFeedback.AppendText(NewLine + "\n\n");
for (int j = 0; j < ConfusionMatrix.Length; j++)
{
NewLine = "";
for (int i = 0; i < ConfusionMatrix[0].Length; i++)
{
NewLine += ConfusionMatrix[j][i] + "\t";
}
// if
TextBoxForFeedback.AppendText(NewLine + "| c" + j + " <=> " + cGlobalInfo.ListCellularPhenotypes[j].Name + "\n");
}
}
#endregion
return this.CurrentClassifier;
}
public cClusteringObject(Classifier Model, Evaluation Evaluation, int NumFolds)
{
this.FoldNumber = NumFolds;
this.Model = Model;
this.Evaluation = Evaluation;
}
/// <summary> Builds the boosted classifier</summary>
public virtual void buildClassifier(Instances data)
{
m_RandomInstance = new Random(m_Seed);
Instances boostData;
int classIndex = data.classIndex();
if (data.classAttribute().Numeric)
{
throw new Exception("LogitBoost can't handle a numeric class!");
}
if (m_Classifier == null)
{
throw new System.Exception("A base classifier has not been specified!");
}
if (!(m_Classifier is WeightedInstancesHandler) && !m_UseResampling)
{
m_UseResampling = true;
}
if (data.checkForStringAttributes())
{
throw new Exception("Cannot handle string attributes!");
}
if (m_Debug)
{
System.Console.Error.WriteLine("Creating copy of the training data");
}
m_NumClasses = data.numClasses();
m_ClassAttribute = data.classAttribute();
// Create a copy of the data
data = new Instances(data);
data.deleteWithMissingClass();
// Create the base classifiers
if (m_Debug)
{
System.Console.Error.WriteLine("Creating base classifiers");
}
m_Classifiers = new Classifier[m_NumClasses][];
for (int j = 0; j < m_NumClasses; j++)
{
m_Classifiers[j] = Classifier.makeCopies(m_Classifier, this.NumIterations);
}
// Do we want to select the appropriate number of iterations
// using cross-validation?
int bestNumIterations = this.NumIterations;
if (m_NumFolds > 1)
{
if (m_Debug)
{
System.Console.Error.WriteLine("Processing first fold.");
}
// Array for storing the results
double[] results = new double[this.NumIterations];
// Iterate throught the cv-runs
for (int r = 0; r < m_NumRuns; r++)
{
// Stratify the data
data.randomize(m_RandomInstance);
data.stratify(m_NumFolds);
// Perform the cross-validation
for (int i = 0; i < m_NumFolds; i++)
{
// Get train and test folds
Instances train = data.trainCV(m_NumFolds, i, m_RandomInstance);
Instances test = data.testCV(m_NumFolds, i);
// Make class numeric
Instances trainN = new Instances(train);
trainN.ClassIndex = - 1;
trainN.deleteAttributeAt(classIndex);
trainN.insertAttributeAt(new weka.core.Attribute("'pseudo class'"), classIndex);
trainN.ClassIndex = classIndex;
m_NumericClassData = new Instances(trainN, 0);
// Get class values
int numInstances = train.numInstances();
double[][] tmpArray = new double[numInstances][];
for (int i2 = 0; i2 < numInstances; i2++)
{
tmpArray[i2] = new double[m_NumClasses];
}
double[][] trainFs = tmpArray;
double[][] tmpArray2 = new double[numInstances][];
for (int i3 = 0; i3 < numInstances; i3++)
{
tmpArray2[i3] = new double[m_NumClasses];
}
double[][] trainYs = tmpArray2;
for (int j = 0; j < m_NumClasses; j++)
{
for (int k = 0; k < numInstances; k++)
{
trainYs[k][j] = (train.instance(k).classValue() == j)?1.0 - m_Offset:0.0 + (m_Offset / (double) m_NumClasses);
}
}
// Perform iterations
double[][] probs = initialProbs(numInstances);
m_NumGenerated = 0;
double sumOfWeights = train.sumOfWeights();
for (int j = 0; j < this.NumIterations; j++)
{
performIteration(trainYs, trainFs, probs, trainN, sumOfWeights);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(this, test);
results[j] += eval.correct();
}
}
}
// Find the number of iterations with the lowest error
//UPGRADE_TODO: The equivalent in .NET for field 'java.lang.Double.MAX_VALUE' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
double bestResult = - System.Double.MaxValue;
for (int j = 0; j < this.NumIterations; j++)
{
if (results[j] > bestResult)
{
bestResult = results[j];
bestNumIterations = j;
}
}
if (m_Debug)
{
System.Console.Error.WriteLine("Best result for " + bestNumIterations + " iterations: " + bestResult);
}
}
// Build classifier on all the data
int numInstances2 = data.numInstances();
double[][] trainFs2 = new double[numInstances2][];
for (int i4 = 0; i4 < numInstances2; i4++)
{
trainFs2[i4] = new double[m_NumClasses];
}
double[][] trainYs2 = new double[numInstances2][];
for (int i5 = 0; i5 < numInstances2; i5++)
{
trainYs2[i5] = new double[m_NumClasses];
}
for (int j = 0; j < m_NumClasses; j++)
{
for (int i = 0, k = 0; i < numInstances2; i++, k++)
{
trainYs2[i][j] = (data.instance(k).classValue() == j)?1.0 - m_Offset:0.0 + (m_Offset / (double) m_NumClasses);
}
}
// Make class numeric
data.ClassIndex = - 1;
data.deleteAttributeAt(classIndex);
data.insertAttributeAt(new weka.core.Attribute("'pseudo class'"), classIndex);
data.ClassIndex = classIndex;
m_NumericClassData = new Instances(data, 0);
// Perform iterations
double[][] probs2 = initialProbs(numInstances2);
double logLikelihood = CalculateLogLikelihood(trainYs2, probs2);
m_NumGenerated = 0;
if (m_Debug)
{
System.Console.Error.WriteLine("Avg. log-likelihood: " + logLikelihood);
}
double sumOfWeights2 = data.sumOfWeights();
for (int j = 0; j < bestNumIterations; j++)
{
double previousLoglikelihood = logLikelihood;
performIteration(trainYs2, trainFs2, probs2, data, sumOfWeights2);
logLikelihood = CalculateLogLikelihood(trainYs2, probs2);
if (m_Debug)
{
System.Console.Error.WriteLine("Avg. log-likelihood: " + logLikelihood);
}
if (System.Math.Abs(previousLoglikelihood - logLikelihood) < m_Precision)
{
return ;
}
}
}
public void EvaluateIncrementalExamples()
{
//Calculate the number of increments for the training data based on the increment size
int numberIncrements = (int)Math.Ceiling((double)(this.numExamples * (DEFAULT_FOLDS - 1) / DEFAULT_FOLDS) / (double)TRAINING_INCRMENETS);
this.trainingSizeMatrix = new double[this.classCount, DEFAULT_FOLDS, numberIncrements];
for (int i = 0; (i < this.classCount); i++)
for (int j = 0; (j < DEFAULT_FOLDS); j++)
for (int k = 0; (k < numberIncrements); k++)
this.trainingSizeMatrix[i, j, k] = 0.0;
//Randomize the data
Randomize randomizeFilter = new Randomize();
randomizeFilter.setInputFormat(this.data);
Instances randomData = Filter.useFilter(this.data, randomizeFilter);
//Run incremental training data for each fold and store the results for each activity
for (int i = 1; (i <= DEFAULT_FOLDS); i++)
{
//Training folds filter
RemoveFolds trainingFoldsFilter = new RemoveFolds();
trainingFoldsFilter.set_NumFolds(DEFAULT_FOLDS);
trainingFoldsFilter.inputFormat(randomData);
trainingFoldsFilter.set_InvertSelection(true);
trainingFoldsFilter.set_Fold(i);
Instances alltraining = Filter.useFilter(randomData, trainingFoldsFilter);
RemoveFolds testFoldsFilter = new RemoveFolds();
testFoldsFilter.set_NumFolds(DEFAULT_FOLDS);
testFoldsFilter.inputFormat(randomData);
testFoldsFilter.set_InvertSelection(false);
testFoldsFilter.set_Fold(i);
Instances test = Filter.useFilter(randomData, testFoldsFilter);
for (int j = 1; (j <= numberIncrements); j++)
{
//Range Filter
RemoveRange rangeFilter = new RemoveRange();
rangeFilter.setInputFormat(alltraining);
int first = 1;
int last = j * TRAINING_INCRMENETS;
if (last > (alltraining.m_Instances.size()))
last = alltraining.m_Instances.size();
string range = first.ToString() + "-" + last.ToString();
rangeFilter.set_InstancesIndices(range);
rangeFilter.set_InvertSelection(true);
Instances training = Filter.useFilter(alltraining, rangeFilter);
//ready for training and testing
J48 tree = new J48(); // new instance of tree
tree.set_MinNumObj(10);
tree.set_ConfidenceFactor((float)0.25);
tree.buildClassifier(training); // build classifier
Evaluation eval = new Evaluation(training);
eval.evaluateModel(tree, test);
//store the results for each activity
for (int k = 0; (k < this.classCount); k++)
{
double tpRate = eval.truePositiveRate(k);
trainingSizeMatrix[k, i - 1, j - 1] = +eval.truePositiveRate(k);
}
}
}
TextWriter tw = new StreamWriter("evaluation.txt");
for (int i = 0; (i < this.classCount); i++)
{
string line = randomData.attribute(this.data.numAttributes() - 1).value_Renamed(i);
for (int k = 0; (k < numberIncrements); k++)
{
double percentage = 0.0;
for (int j = 0; (j < DEFAULT_FOLDS); j++)
percentage += this.trainingSizeMatrix[i, j, k];
percentage /= DEFAULT_FOLDS;
percentage *= 100;
line += "," + percentage.ToString("0.00");
}
tw.WriteLine(line);
}
tw.Close();
for (int i = 0; (i < this.classCount); i++)
{
string activity = randomData.attribute(this.data.numAttributes() - 1).value_Renamed(i);
tw = new StreamWriter("evaluation-" + activity + ".txt");
for (int j = 0; (j < DEFAULT_FOLDS); j++)
{
string line = j.ToString();
for (int k = 0; (k < numberIncrements); k++)
{
double percentage = this.trainingSizeMatrix[i, j, k];
percentage /= DEFAULT_FOLDS;
percentage *= 100;
line += "\t" + percentage.ToString("0.00");
}
tw.WriteLine(line);
}
tw.Close();
}
}
public static void Test_predictClass(string classifierFileName)
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances insts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
#region Manual Cross Fold
Instances foldsData = new Instances(insts);
int folds = 10;
for (int n = 0; n < folds; n++)
{
Instances trainFold = foldsData.trainCV(folds, n);
Instances testFold = foldsData.testCV(folds, n);
}
#endregion
#region
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
#endregion
//Classifier cls = new J48();
Evaluation eval = new Evaluation(insts);
java.util.Random rand = new java.util.Random(1); // using seed = 1
int fold = 10;
eval.crossValidateModel(cl, insts, fold, rand);
System.Console.WriteLine("toClassDetailsString" + eval.toClassDetailsString());
System.Console.WriteLine("toMatrixString\n" + eval.toMatrixString());
System.Console.WriteLine("toCumulativeMarginDistributionString\n" + eval.toCumulativeMarginDistributionString());
//System.Console.WriteLine("predictions\n" + eval.predictions());
System.Console.ReadKey();
//var numnerOfInst = insts.numInstances();
//for (int i = trainSize; i < numnerOfInst; i++)
//{
// weka.core.Instance currentInst = insts.instance(i);
// double pred = cl.classifyInstance(currentInst);
// System.Console.WriteLine("class Index: " + insts.instance(i).classIndex());
// System.Console.WriteLine(", class value: " + insts.instance(i).classValue());
// System.Console.WriteLine(", ID: " + insts.instance(i).value(0));
// System.Console.WriteLine(", actual: " + insts.classAttribute().value((int)insts.instance(i).classValue()));
// System.Console.WriteLine(", predicted: " + insts.classAttribute().value((int)pred));
//}
}
