public static void Main(string[] args)
{
// Create a training set
IList <IDatum <string, string> > trainingData = new List <IDatum <string, string> >();
trainingData.Add(MakeStopLights(Green, Red));
trainingData.Add(MakeStopLights(Green, Red));
trainingData.Add(MakeStopLights(Green, Red));
trainingData.Add(MakeStopLights(Red, Green));
trainingData.Add(MakeStopLights(Red, Green));
trainingData.Add(MakeStopLights(Red, Green));
trainingData.Add(MakeStopLights(Red, Red));
// Create a test set
IDatum <string, string> workingLights = MakeStopLights(Green, Red);
IDatum <string, string> brokenLights = MakeStopLights(Red, Red);
// Build a classifier factory
LinearClassifierFactory <string, string> factory = new LinearClassifierFactory <string, string>();
factory.UseConjugateGradientAscent();
// Turn on per-iteration convergence updates
factory.SetVerbose(true);
//Small amount of smoothing
factory.SetSigma(10.0);
// Build a classifier
LinearClassifier <string, string> classifier = factory.TrainClassifier(trainingData);
// Check out the learned weights
classifier.Dump();
// Test the classifier
System.Console.Out.WriteLine("Working instance got: " + classifier.ClassOf(workingLights));
classifier.JustificationOf(workingLights);
System.Console.Out.WriteLine("Broken instance got: " + classifier.ClassOf(brokenLights));
classifier.JustificationOf(brokenLights);
}
public static void TestDataset()
{
Dataset <string, string> data = new Dataset <string, string>();
data.Add(new BasicDatum <string, string>(Arrays.AsList(new string[] { "fever", "cough", "congestion" }), "cold"));
data.Add(new BasicDatum <string, string>(Arrays.AsList(new string[] { "fever", "cough", "nausea" }), "flu"));
data.Add(new BasicDatum <string, string>(Arrays.AsList(new string[] { "cough", "congestion" }), "cold"));
// data.summaryStatistics();
NUnit.Framework.Assert.AreEqual(4, data.NumFeatures());
NUnit.Framework.Assert.AreEqual(4, data.NumFeatureTypes());
NUnit.Framework.Assert.AreEqual(2, data.NumClasses());
NUnit.Framework.Assert.AreEqual(8, data.NumFeatureTokens());
NUnit.Framework.Assert.AreEqual(3, data.Size());
data.ApplyFeatureCountThreshold(2);
NUnit.Framework.Assert.AreEqual(3, data.NumFeatures());
NUnit.Framework.Assert.AreEqual(3, data.NumFeatureTypes());
NUnit.Framework.Assert.AreEqual(2, data.NumClasses());
NUnit.Framework.Assert.AreEqual(7, data.NumFeatureTokens());
NUnit.Framework.Assert.AreEqual(3, data.Size());
//Dataset data = Dataset.readSVMLightFormat(args[0]);
//double[] scores = data.getInformationGains();
//System.out.println(ArrayMath.mean(scores));
//System.out.println(ArrayMath.variance(scores));
LinearClassifierFactory <string, string> factory = new LinearClassifierFactory <string, string>();
LinearClassifier <string, string> classifier = factory.TrainClassifier(data);
IDatum <string, string> d = new BasicDatum <string, string>(Arrays.AsList(new string[] { "cough", "fever" }));
NUnit.Framework.Assert.AreEqual("Classification incorrect", "flu", classifier.ClassOf(d));
ICounter <string> probs = classifier.ProbabilityOf(d);
NUnit.Framework.Assert.AreEqual("Returned probability incorrect", 0.4553, probs.GetCount("cold"), 0.0001);
NUnit.Framework.Assert.AreEqual("Returned probability incorrect", 0.5447, probs.GetCount("flu"), 0.0001);
System.Console.Out.WriteLine();
}
/// <exception cref="System.IO.IOException"/>
/// <exception cref="System.TypeLoadException"/>
private static void DemonstrateSerialization()
{
System.Console.Out.WriteLine();
System.Console.Out.WriteLine("Demonstrating working with a serialized classifier");
ColumnDataClassifier cdc = new ColumnDataClassifier(where + "examples/cheese2007.prop");
IClassifier <string, string> cl = cdc.MakeClassifier(cdc.ReadTrainingExamples(where + "examples/cheeseDisease.train"));
// Exhibit serialization and deserialization working. Serialized to bytes in memory for simplicity
System.Console.Out.WriteLine();
ByteArrayOutputStream baos = new ByteArrayOutputStream();
ObjectOutputStream oos = new ObjectOutputStream(baos);
oos.WriteObject(cl);
oos.Close();
byte[] @object = baos.ToByteArray();
ByteArrayInputStream bais = new ByteArrayInputStream(@object);
ObjectInputStream ois = new ObjectInputStream(bais);
LinearClassifier <string, string> lc = ErasureUtils.UncheckedCast(ois.ReadObject());
ois.Close();
ColumnDataClassifier cdc2 = new ColumnDataClassifier(where + "examples/cheese2007.prop");
// We compare the output of the deserialized classifier lc versus the original one cl
// For both we use a ColumnDataClassifier to convert text lines to examples
System.Console.Out.WriteLine();
System.Console.Out.WriteLine("Making predictions with both classifiers");
foreach (string line in ObjectBank.GetLineIterator(where + "examples/cheeseDisease.test", "utf-8"))
{
IDatum <string, string> d = cdc.MakeDatumFromLine(line);
IDatum <string, string> d2 = cdc2.MakeDatumFromLine(line);
System.Console.Out.Printf("%s =origi=> %s (%.4f)%n", line, cl.ClassOf(d), cl.ScoresOf(d).GetCount(cl.ClassOf(d)));
System.Console.Out.Printf("%s =deser=> %s (%.4f)%n", line, lc.ClassOf(d2), lc.ScoresOf(d).GetCount(lc.ClassOf(d)));
}
}
/// <summary>Train a multinomial classifier off of the provided dataset.</summary>
/// <param name="dataset">The dataset to train the classifier off of.</param>
/// <returns>A classifier.</returns>
public static IClassifier <string, string> TrainMultinomialClassifier(GeneralDataset <string, string> dataset, int featureThreshold, double sigma)
{
// Set up the dataset and factory
log.Info("Applying feature threshold (" + featureThreshold + ")...");
dataset.ApplyFeatureCountThreshold(featureThreshold);
log.Info("Randomizing dataset...");
dataset.Randomize(42l);
log.Info("Creating factory...");
LinearClassifierFactory <string, string> factory = InitFactory(sigma);
// Train the final classifier
log.Info("BEGIN training");
LinearClassifier <string, string> classifier = factory.TrainClassifier(dataset);
log.Info("END training");
// Debug
KBPRelationExtractor.Accuracy trainAccuracy = new KBPRelationExtractor.Accuracy();
foreach (IDatum <string, string> datum in dataset)
{
string guess = classifier.ClassOf(datum);
trainAccuracy.Predict(Java.Util.Collections.Singleton(guess), Java.Util.Collections.Singleton(datum.Label()));
}
log.Info("Training accuracy:");
log.Info(trainAccuracy.ToString());
log.Info(string.Empty);
// Return the classifier
return(classifier);
}