// Public Methods
public override double ExecuteCommand()
{
FeatureVectorFile vectorFile_train = new FeatureVectorFile(path: training_data_file, noOfHeaderColumns: 1, featureDelimiter: ' ', isSortRequired: true);
FeatureVectorFile vectorFile_test = new FeatureVectorFile(path: test_data_file, noOfHeaderColumns: 1, featureDelimiter: ' ', isSortRequired: true);
int gold_i = 0;
TextIdMapper featureToFeatureId = new TextIdMapper();
TextIdMapper classToClassId = new TextIdMapper();
TextIdMapper[] headerToHeaderIds = new TextIdMapper[] { classToClassId };
var trainingVectors = vectorFile_train.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Binary);
var goldClasses_train = vectorFile_train.Headers[gold_i];
var testVectors = vectorFile_test.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Binary);
var goldClasses_test = vectorFile_test.Headers[gold_i];
var classifier = new kNNClassifier(k_val, (SimilarityFunction)similarity_func, trainingVectors, classToClassId.Count, gold_i);
var systemClasses_train = classifier.Classify(trainingVectors);
var systemClasses_test = classifier.Classify(testVectors);
var details_train = ProgramOutput.GetDistributionDetails(classifier, trainingVectors, classToClassId);
var details_test = ProgramOutput.GetDistributionDetails(classifier, testVectors, classToClassId);
ProgramOutput.GenerateSysOutput(sys_output, FileCreationMode.CreateNew, trainingVectors, classToClassId, goldClasses_train, systemClasses_train, details_train, "training data");
var testAccuracy = ProgramOutput.GenerateSysOutput(sys_output, FileCreationMode.Append, testVectors, classToClassId, goldClasses_test, systemClasses_test, details_test, "test data");
return(testAccuracy);
}
// Methods
public override double ExecuteCommand()
{
int instanceName_i = 0;
int gold_i = 1;
featureToFeatureId = new TextIdMapper();
classToClassId = new TextIdMapper();
var instanceNameToId = new TextIdMapper();
TextIdMapper[] headerToHeaderIds = new TextIdMapper[] { instanceNameToId, classToClassId };
FeatureVectorFile vectorFile = new FeatureVectorFile(path: vector_file, noOfHeaderColumns: 2, featureDelimiter: ' ', isSortRequired: false);
// Read the boundaries:
int[] sentenceLengths = ReadBoundaryFile(boundary_file);
// Read the classifier model:
classifier = MaxEntPOSClassifier.LoadModel(model_file, classToClassId, featureToFeatureId);
// Read the vectors:
var testVectors = vectorFile.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Continuous);
// Get the output ready for display.
int[] goldClassIds = vectorFile.Headers[gold_i];
int[] instanceNameIds = vectorFile.Headers[instanceName_i];
string[] instanceNames = instanceNameToId.GetValues(instanceNameIds);
// Generate sys_output:
ConfusionMatrix confusionMatrix;
File.WriteAllText(sys_output, GenerateSysOutput(instanceNames, testVectors, sentenceLengths, out confusionMatrix, gold_i));
return(confusionMatrix.CalculateAccuracy());
}
/// <summary>
/// Loads the classifier provided by the <c>classifierFactory</c> which is modelled using the specified <c>model_file</c>.
/// Then, the classifier is used to evaluate the accuracy of the <c>vector_data</c>.
/// A report on the classification details is printed to the <c>output_file</c>.
/// </summary>
/// <param name="model_file">A file containing a serialization of the classifier model.</param>
/// <param name="sys_output">A report on the classification details.</param>
/// <param name="classifierFactory">Provides the necessary classifier.</param>
internal static double ReportOnModel(
FeatureVectorFile vectorFile
, string sys_output
, Func <TextIdMapper, TextIdMapper, Classifier> classifierFactory
, Func <Classifier, List <FeatureVector>, TextIdMapper, TextIdMapper, string[]> getDetailsFunc
)
{
int gold_i = 0;
TextIdMapper featureToFeatureId = new TextIdMapper();
TextIdMapper classToClassId = new TextIdMapper();
TextIdMapper[] headerToHeaderIds = new TextIdMapper[] { classToClassId };
Classifier classifier = classifierFactory(classToClassId, featureToFeatureId);
var vectors = vectorFile.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Binary);
var goldClasses = vectorFile.Headers[gold_i];
var systemClasses = classifier.Classify(vectors);
string[] details = getDetailsFunc(classifier, vectors, classToClassId, featureToFeatureId);
var accuracy = ProgramOutput.GenerateSysOutput(sys_output, FileCreationMode.CreateNew, vectors, classToClassId, goldClasses, systemClasses, details, heading: Path.GetFileName(vectorFile.Path));
return(accuracy);
}
/// <summary>
/// Trains the classifier provided by the <c>classifierFactory</c> on the <c>training_data</c>.
/// Then, the classifier is used to evaluate the accuracy of both the <c>training_data</c> and <c>test_data</c>.
/// A report on the classification details is printed to the <c>output_file</c>.
/// </summary>
/// <param name="output_file">A report on the classification details.</param>
/// <param name="classifierFactory">Provides the necessary classifier.</param>
internal static void ReportOnTrainingAndTesting(
FeatureVectorFile vectorFile_train
, FeatureVectorFile vectorFile_test
, string output_file
, Func <List <FeatureVector>, int, int, Classifier> classifierFactory
, Func <Classifier, List <FeatureVector>, TextIdMapper, string[]> getDetailsFunc
)
{
int gold_i = 0;
TextIdMapper featureToFeatureId = new TextIdMapper();
TextIdMapper classToClassId = new TextIdMapper();
TextIdMapper[] headerToHeaderIds = new TextIdMapper[] { classToClassId };
var trainingVectors = vectorFile_train.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Binary);
var goldClasses_train = vectorFile_train.Headers[gold_i];
var testVectors = vectorFile_test.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Binary);
var goldClasses_test = vectorFile_test.Headers[gold_i];
Classifier classifier = classifierFactory(trainingVectors, classToClassId.Count, gold_i);
var systemClasses_train = classifier.Classify(trainingVectors);
var systemClasses_test = classifier.Classify(testVectors);
var details_train = ProgramOutput.GetDistributionDetails(classifier, trainingVectors, classToClassId);
var details_test = ProgramOutput.GetDistributionDetails(classifier, testVectors, classToClassId);
ProgramOutput.GenerateSysOutput(output_file, FileCreationMode.CreateNew, trainingVectors, classToClassId, goldClasses_train, systemClasses_train, details_train, "training data");
ProgramOutput.GenerateSysOutput(output_file, FileCreationMode.Append, testVectors, classToClassId, goldClasses_test, systemClasses_test, details_test, "test data");
}
// Methods
public override bool ExecuteCommand()
{
// Initialize the text-to-Id mappers:
int gold_i = 0;
featureToFeatureId = new TextIdMapper();
classToClassId = new TextIdMapper();
TextIdMapper[] headerToHeaderIds = new TextIdMapper[] { classToClassId };
// Workaround: Read everything from STDIN to a file. (Files are used as the text source throughout this application.)
var svmLight_data = Console.In.ReadToEnd();
Console.Error.WriteLine("{0} characters of input received.", svmLight_data.Length);
string tempFile = Path.GetTempFileName();
int[] goldClasses;
List <FeatureVector> vectors;
try
{
File.WriteAllText(tempFile, svmLight_data);
FeatureVectorFile vectorFile = new FeatureVectorFile(path: tempFile, noOfHeaderColumns: 1, featureDelimiter: ' ', isSortRequired: false);
vectors = vectorFile.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Binary);
goldClasses = vectorFile.Headers[gold_i];
}
finally
{
File.Delete(tempFile);
}
Debug.Assert(vectors.Count > 0);
IdValuePair <double>[] chiSquare = new IdValuePair <double> [featureToFeatureId.Count];
//TODO: Make the implementation less binary dependent (i.e. the hardcoded 2 below).
double[][,] contingencyTable_f = new double[featureToFeatureId.Count][, ];
for (int f_i = 0; f_i < featureToFeatureId.Count; f_i++)
{
// Create a contingency table for this vector.
contingencyTable_f[f_i] = new double[classToClassId.Count, 2];
for (int v_i = 0; v_i < vectors.Count; v_i++)
{
FeatureVector v = vectors[v_i];
contingencyTable_f[f_i][v.Headers[gold_i], (int)v.Features[f_i]]++;
}
chiSquare[f_i] = new IdValuePair <double>(f_i, StatisticsHelper.CalculateChiSquare(contingencyTable_f[f_i]));
}
ReportChiSquareResults(contingencyTable_f, chiSquare);
return(true);
}
// Methods
public override double ExecuteCommand()
{
FeatureVectorFile vectorFile = new FeatureVectorFile(path: vector_file, noOfHeaderColumns: 1, featureDelimiter: ':', isSortRequired: false);
// Initialize the text-to-Id mappers:
featureToFeatureId = new TextIdMapper();
int instanceName_i = 0;
int gold_i = 1;
classToClassId = new TextIdMapper();
var instanceNameToInstanceNameId = new TextIdMapper();
TextIdMapper[] headerToHeaderIds = new TextIdMapper[]
{
instanceNameToInstanceNameId
, classToClassId
};
// Read the boundaries:
int[] sentenceLengths = ReadBoundaryFile(boundary_file);
// Read the classifier model:
classifier = MaxEntPOSClassifier.LoadModel(model_file, classToClassId, featureToFeatureId);
// Read the vectors:
var testVectors = vectorFile.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Continuous);
int[] instanceNameIds = vectorFile.Headers[instanceName_i];
int[] goldClasses = vectorFile.Headers[gold_i];
// TODO: Neaten this up a little.
string[] instanceNames = new string[instanceNameIds.Length];
for (int i = 0; i < instanceNameIds.Length; i++)
{
int instanceNameId = instanceNameIds[i];
instanceNames[i] = headerToHeaderIds[instanceName_i][i];
}
// Generate sys_output:
var confusionMatrix = GenerateSysOutput(sys_output, instanceNames, testVectors, sentenceLengths, gold_i);
return(confusionMatrix.CalculateAccuracy());
}
// Methods
public override bool ExecuteCommand()
{
FeatureVectorFile vectorFile = new FeatureVectorFile(path: vector_file, noOfHeaderColumns: 1, featureDelimiter: ' ', isSortRequired: false);
// Load the training file.
int gold_i = 0;
TextIdMapper featureToFeatureId = new TextIdMapper();
TextIdMapper classToClassId = new TextIdMapper();
TextIdMapper[] headerToHeaderIds = new TextIdMapper[] { classToClassId };
var trainingVectors = vectorFile.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Binary);
var goldClasses = vectorFile.Headers[gold_i];
double[,] observation, expectation;
CalculateObservationAndEmpiricalExpectation(trainingVectors, out observation, out expectation);
OutputEmpiricalCount(observation, expectation);
return(true);
}
/// <summary>
/// Trains a classifier on the specified <c>train_data</c>.
/// Output the model to the specified <c>model_file</c>.
/// </summary>
/// <param name="model_file">A file containing a serialization of the classifier model.</param>
/// <param name="classifierFactory">Provides the necessary classifier, which must implement ISaveModel.</param>
internal static void TrainModel <T>(
FeatureVectorFile vector_file
, string model_file
, Func <List <FeatureVector>, TextIdMapper, TextIdMapper, T> classifierFactory
)
where T : Classifier, ISaveModel
{
TextIdMapper featureToFeatureId = new TextIdMapper();
TextIdMapper classToClassId = new TextIdMapper();
TextIdMapper[] headerToHeaderIds = new TextIdMapper[] { classToClassId };
var vectors = vector_file.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Binary);
T classifier = classifierFactory(vectors, classToClassId, featureToFeatureId);
//var systemClasses =
classifier.Classify(vectors);
classifier.SaveModel(model_file, classToClassId, featureToFeatureId);
}
// Methods
public override bool ExecuteCommand()
{
FeatureVectorFile vectorFile_train = new FeatureVectorFile(path: training_data_file, noOfHeaderColumns: 1, featureDelimiter: ' ', isSortRequired: false);
int gold_i = 0;
TextIdMapper featureToFeatureId = new TextIdMapper();
TextIdMapper classToClassId = new TextIdMapper();
TextIdMapper[] headerToHeaderIds = new TextIdMapper[] { classToClassId };
var trainingVectors = vectorFile_train.LoadFromSVMLight(featureToFeatureId, headerToHeaderIds, FeatureType.Binary);
var goldClasses_train = vectorFile_train.Headers[gold_i];
// model_file is optional.
Func <int, FeatureVector, double> calculate_Prob_c_v;
// If it is not given, p(v|c_i) = 1/|C|, where |C| is the number of class_labels.
if (string.IsNullOrWhiteSpace(model_file))
{
double kProbability = 1D / classToClassId.Count;
calculate_Prob_c_v = (v, c_i) => { return(kProbability); };
}
// If it is given, it is used to calculate p(y|xi).
else
{
MaxEntClassifier classifier = MaxEntClassifier.LoadModel(model_file, classToClassId, featureToFeatureId);
calculate_Prob_c_v =
(c_i, v) =>
{
double[] details;
int sysClass = classifier.Classify(v, out details);
return(details[c_i]);
};
}
double[,] expectation = CalculateModelExpectation(trainingVectors, calculate_Prob_c_v);
OutputEmpiricalCount(expectation, trainingVectors.Count, requiresSort: true);
return(true);
}