// Called when the user tries to open a file to test the classifier.
private void testingFile_button_Click(object sender, EventArgs e)
{
// Scroll to the end of the text box.
classificationLog_richTextBox.SelectionStart = classificationLog_richTextBox.Text.Length;
classificationLog_richTextBox.ScrollToCaret();
if (testing_openFileDialog.ShowDialog() == DialogResult.OK)
{
testingData = new ClassificationData();
// Return if file was not open and parsed correctly.
if (!testingData.OpenAndParseFile(testing_openFileDialog.FileName, true))
{
classificationLog_richTextBox.SelectionColor = Color.Red;
classificationLog_richTextBox.SelectedText =
"=====> Testing file: " +
testing_openFileDialog.FileName +
" was not opened or parsed correctly." +
Environment.NewLine;
return;
}
testingFileLoaded = true;
classificationLog_richTextBox.SelectionColor = Color.Green;
classificationLog_richTextBox.SelectedText =
"=====> Testing file: " +
testing_openFileDialog.FileName +
" was opened successfully." +
Environment.NewLine;
reset_button.Enabled = true;
testClassifier_button.Enabled = trainingFileLoaded && testingFileLoaded && dataTrained;
}
}
/// <summary>
/// Test the classifier with some data.
/// </summary>
/// <param name="testingData">Data used to test the classifier.</param>
/// <returns>Array of predicted values.</returns>
public override int[] TestClassifier(ClassificationData testingData)
{
List <int> results = new List <int>();
// Predict the results for a series of inputs.
foreach (double[] input in testingData.InputData)
{
results.Add(ClassificationDecisionTree.Compute(input));
}
return(results.ToArray());
}
// Reset all controls.
private void reset_button_Click(object sender, EventArgs e)
{
if (treeVisualizer != null)
{
treeVisualizer.Close();
}
if (matrixVisualizer != null)
{
matrixVisualizer.Close();
}
trainingFileLoaded = false;
testingFileLoaded = false;
dataTrained = false;
attributeChosen = false;
classifierChosen = false;
classifierError = 0;
predictionError = 0;
classifier = null;
trainingData = null;
testingData = null;
parameters_groupBox.Enabled = false;
treeParameters_panel.Visible = false;
bayesianParameters_panel.Visible = false;
svmParameters_panel.Visible = false;
treeJoin_numericUpDown.Value = 0;
treeJoin_numericUpDown.Enabled = true;
treeHeight_numericUpDown.Value = 0;
treeHeight_numericUpDown.Enabled = true;
svmKernel_comboBox.SelectedIndex = 0;
svmKernel_comboBox.Enabled = true;
svmAlgorithm_comboBox.SelectedIndex = 0;
svmAlgorithm_comboBox.Enabled = true;
decisionTree_checkBox.Enabled = true;
attributeToPredict_comboBox.DataSource = null;
attributeToPredict_comboBox.Enabled = false;
classifierToUse_comboBox.DataSource = null;
classifierToUse_comboBox.Enabled = false;
trainingFile_button.Enabled = true;
trainClassifier_button.Enabled = false;
testClassifier_button.Enabled = false;
dataset_dataGridView.DataSource = null;
dataset_dataGridView.Enabled = false;
trainingTimeValue_label.ForeColor = SystemColors.ControlText;
testingTimeValue_label.ForeColor = SystemColors.ControlText;
classifierErrorValue_label.ForeColor = SystemColors.ControlText;
predictionErrorValue_label.ForeColor = SystemColors.ControlText;
trainingTimeValue_label.Text = "-";
testingTimeValue_label.Text = "-";
classifierErrorValue_label.Text = "-";
predictionErrorValue_label.Text = "-";
classificationLog_richTextBox.Clear();
classificationLog_richTextBox.Enabled = false;
}
/// <summary>
/// Test the classifier with some data.
/// </summary>
/// <param name="testingData">Data used to test the classifier.</param>
/// <returns>Array of predicted values.</returns>
public override int[] TestClassifier(ClassificationData testingData)
{
List <int> results = new List <int>();
// Predict the results for a series of inputs.
foreach (double[] input in testingData.InputData)
{
results.Add(SVMachine.Compute(input, MulticlassComputeMethod.Voting));
}
return(results.ToArray());
}
/// <summary>
/// Train the classifier with some data, using parameters.
/// </summary>
/// <param name="trainingData">Data used to train the classifier.</param>
/// <param name="maxJoin">How many times a variable can join
/// the decision process.</param>
/// <param name="maxHeight">Maximum height when learning the tree.</param>
/// <returns>Classifier prediction error.</returns>
public double TrainClassifierWithParameters(
ClassificationData trainingData,
int maxJoin = 0,
int maxHeight = 0)
{
double classifierError = 0;
List <DecisionVariable> decisionVariables = new List <DecisionVariable>();
if (DecisionVariableNames != null)
{
for (int n = 0; n < trainingData.InputAttributeNumber; ++n)
{
decisionVariables.Add(
new DecisionVariable(DecisionVariableNames[n], DecisionVariableKind.Continuous)
);
}
}
// Generate automatic names for the variables if no names are provided.
else
{
for (int n = 0; n < trainingData.InputAttributeNumber; ++n)
{
decisionVariables.Add(
new DecisionVariable("variable_" + (n + 1).ToString(),
DecisionVariableKind.Continuous));
}
}
// Create a new Decision Tree classifier.
ClassificationDecisionTree = new DecisionTree(decisionVariables, trainingData.OutputPossibleValues);
// Create a new instance of the C45 algorithm to be learned by the tree.
C45LearningTree = new C45Learning(ClassificationDecisionTree);
// Change some classifier's parameters if valid new
// values are provided.
if (maxJoin > 0)
{
C45LearningTree.Join = maxJoin;
}
if (maxHeight > 0)
{
C45LearningTree.MaxHeight = maxHeight;
}
// Use data to train the tree.
classifierError = C45LearningTree.Run(trainingData.InputData, trainingData.OutputData);
return(classifierError);
}
/// <summary>
/// Default constructor.
/// </summary>
/// <param name="testingData">Source data containing the actual values.</param>
/// <param name="predictedValues">Predicted results for the source data.</param>
public ConfusionMatrixView(ClassificationData testingData, int[] predictedValues)
{
InitializeComponent();
// Create a confusion matrix using source and predicted data.
GeneralConfusionMatrix confusionMatrix =
new GeneralConfusionMatrix(testingData.OutputPossibleValues, predictedValues, testingData.OutputData);
// The confusion matrix will be shown as a DataTable,
// in which columns represent the actual values while
// rows represent the predicted values. The first column
// holds the names of the values.
// Create the structure of the table.
for (int n = 0; n <= testingData.OutputPossibleValues; ++n)
{
if (n == 0)
{
confusionMatrixTable.Columns.Add("Confusion matrix", typeof(string));
}
else
{
confusionMatrixTable.Columns.Add(
testingData.CodeBook.Translate(testingData.OutputColumnName, n - 1),
typeof(int));
}
}
// Populate the table rows with data.
for (int i = 0; i < testingData.OutputPossibleValues; ++i)
{
DataRow newRow = confusionMatrixTable.NewRow();
for (int j = 0; j <= testingData.OutputPossibleValues; ++j)
{
if (j == 0)
{
newRow[j] = testingData.CodeBook.Translate(testingData.OutputColumnName, i);
}
else
{
newRow[j] = confusionMatrix.Matrix[i, j - 1];
}
}
confusionMatrixTable.Rows.Add(newRow);
}
confusionMatrix_dataGridView.DataSource = confusionMatrixTable;
}
// Called when the user tries to open a file to train the classifier.
private void trainingFile_button_Click(object sender, EventArgs e)
{
// Scroll to the end of the text box.
classificationLog_richTextBox.SelectionStart = classificationLog_richTextBox.Text.Length;
classificationLog_richTextBox.ScrollToCaret();
if (training_openFileDialog.ShowDialog() == DialogResult.OK)
{
trainingData = new ClassificationData();
// Return if the file was not open and parsed correctly.
if (!trainingData.OpenAndParseFile(training_openFileDialog.FileName, true))
{
classificationLog_richTextBox.SelectionColor = Color.Red;
classificationLog_richTextBox.SelectedText =
"=====> Training file: " +
training_openFileDialog.FileName +
" was not opened or parsed correctly." +
Environment.NewLine;
return;
}
trainingFileLoaded = true;
classificationLog_richTextBox.SelectionColor = Color.Green;
classificationLog_richTextBox.SelectedText =
"=====> Training file: " +
training_openFileDialog.FileName +
" was opened successfully. " +
Environment.NewLine;
// Activate some window's controls if the file was loaded successfully.
attributeToPredict_comboBox.DataSource = trainingData.AllColumnNames;
attributeToPredict_comboBox.SelectedIndex = -1;
attributeToPredict_comboBox.Enabled = true;
classifierToUse_comboBox.DataSource = classificationMethods;
classifierToUse_comboBox.SelectedIndex = -1;
classifierToUse_comboBox.Enabled = true;
dataset_dataGridView.DataSource = trainingData.ExtractedDataset;
dataset_dataGridView.Enabled = true;
classificationLog_richTextBox.Enabled = true;
trainClassifier_button.Enabled = attributeChosen && classifierChosen;
reset_button.Enabled = true;
testClassifier_button.Enabled = trainingFileLoaded && testingFileLoaded && dataTrained;
}
}
public double TrainClassifierWithParameters(
ClassificationData trainingData,
IKernel kernel = null,
SupportVectorMachineLearningConfigurationFunction algorithm = null)
{
double classifierError = 0;
// Use default parameters if no valid new
// values are provided.
if (kernel == null)
{
kernel = new Linear();
}
if (algorithm == null)
{
algorithm = (SVM, inputData, outputData, i, j) =>
new SequentialMinimalOptimization(SVM, inputData, outputData)
{
// Avoid OutOfMemory exception.
CacheSize = 1000
}
}
;
// Create a new SVM classifier.
SVMachine = new MulticlassSupportVectorMachine(
trainingData.InputAttributeNumber,
kernel,
trainingData.OutputPossibleValues);
// Create an algorithm to be learned by the SVM.
SVMachineLearning = new MulticlassSupportVectorLearning(
SVMachine,
trainingData.InputData,
trainingData.OutputData);
SVMachineLearning.Algorithm = algorithm;
// Run the learning algorithm.
classifierError = SVMachineLearning.Run(true);
return(classifierError);
}
/// <summary>
/// Train the classifier with some data.
/// </summary>
/// <param name="trainingData">Data used to train the classifier.</param>
/// <returns>Classifier prediction error.</returns>
public override double TrainClassifier(ClassificationData trainingData)
{
double classifierError = 0;
// Create a new Naive Bayes classifier.
BayesianModel = new NaiveBayes <NormalDistribution>(
trainingData.OutputPossibleValues,
trainingData.InputAttributeNumber,
NormalDistribution.Standard);
// Compute the Naive Bayes model.
classifierError = BayesianModel.Estimate(
trainingData.InputData,
trainingData.OutputData,
true,
new NormalOptions {
Regularization = 1e-5 /* To avoid zero variances. */
});
return(classifierError);
}
/// <summary>
/// Train the classifier with some data and default parameters.
/// </summary>
/// <param name="trainingData">Data used to train the classifier.</param>
/// <returns>Classifier prediction error.</returns>
public override double TrainClassifier(ClassificationData trainingData)
{
// Train the classifier using default parameters.
return(TrainClassifierWithParameters(trainingData));
}
