/// <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 ;
}
}
}
/// <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();
}
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();
}
}
