added multi label examples

BrightWire.Source/ExecutionGraph/WireBuilder.cs

@@ -91,9 +91,8 @@ public WireBuilder IncrementSizeBy(int delta)
 
         void _SetNode(INode node)
         {
-            if (_node != null)
-                _node.Output.Add(new WireToNode(node));
-            _node = node;
+	        _node?.Output.Add(new WireToNode(node));
+	        _node = node;
         }
 
         /// <summary>

SampleCode/MultiLabel.cs

@@ -0,0 +1,189 @@
+using BrightWire.ExecutionGraph;
+using BrightWire.Models;
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using System.Text;
+using System.Threading.Tasks;
+
+namespace BrightWire.SampleCode
+{
+	public partial class Program
+	{
+		const int CLASSIFICATION_COUNT = 6;
+
+		static IDataTable _LoadEmotionData(string dataFilePath)
+		{
+			// read the data as CSV, skipping the header
+			using (var reader = new StreamReader(dataFilePath)) {
+				while (!reader.EndOfStream) {
+					var line = reader.ReadLine();
+					if (line == "@data")
+						break;
+				}
+				return reader.ReadToEnd().ParseCSV(',', false);
+			}
+		}
+
+		/// <summary>
+		/// Trains a feed forward neural net on the emotion dataset
+		/// http://lpis.csd.auth.gr/publications/tsoumakas-ismir08.pdf
+		/// The data files can be downloaded from https://downloads.sourceforge.net/project/mulan/datasets/emotions.rar
+		/// </summary>
+		/// <param name="dataFilePath"></param>
+		public static void MultiLabelSingleClassifier(string dataFilePath)
+		{
+			var emotionData = _LoadEmotionData(dataFilePath);
+			var attributeColumns = Enumerable.Range(0, emotionData.ColumnCount - CLASSIFICATION_COUNT).ToList();
+			var classificationColumns = Enumerable.Range(emotionData.ColumnCount - CLASSIFICATION_COUNT, CLASSIFICATION_COUNT).ToList();
+
+			// create a new data table with a vector input column and a vector output column
+			var dataTableBuilder = BrightWireProvider.CreateDataTableBuilder();
+			dataTableBuilder.AddColumn(ColumnType.Vector, "Attributes");
+			dataTableBuilder.AddColumn(ColumnType.Vector, "Target", isTarget: true);
+			emotionData.ForEach(row => {
+				var input = FloatVector.Create(row.GetFields<float>(attributeColumns).ToArray());
+				var target = FloatVector.Create(row.GetFields<float>(classificationColumns).ToArray());
+				dataTableBuilder.Add(input, target);
+				return true;
+			});
+			var data = dataTableBuilder.Build().Split(0);
+
+			// train a neural network
+			using (var lap = BrightWireProvider.CreateLinearAlgebra(false)) {
+				var graph = new GraphFactory(lap);
+
+				// binary classification rounds each output to 0 or 1 and compares each output against the binary classification targets
+				var errorMetric = graph.ErrorMetric.BinaryClassification;
+
+				// configure the network properties
+				graph.CurrentPropertySet
+					.Use(graph.GradientDescent.Adam)
+					.Use(graph.WeightInitialisation.Xavier)
+				;
+
+				// create a training engine
+				const float TRAINING_RATE = 0.3f;
+				var trainingData = graph.CreateDataSource(data.Training);
+				var testData = trainingData.CloneWith(data.Test);
+				var engine = graph.CreateTrainingEngine(trainingData, TRAINING_RATE, 128);
+
+				// build the network
+				const int HIDDEN_LAYER_SIZE = 64, TRAINING_ITERATIONS = 2000;
+				var network = graph.Connect(engine)
+					.AddFeedForward(HIDDEN_LAYER_SIZE)
+					.Add(graph.SigmoidActivation())
+					.AddDropOut(dropOutPercentage: 0.5f)
+					.AddFeedForward(engine.DataSource.OutputSize)
+					.Add(graph.SigmoidActivation())
+					.AddBackpropagation(errorMetric)
+				;
+
+				// train the network
+				engine.Train(TRAINING_ITERATIONS, testData, errorMetric, null, 50);
+			}
+		}
+
+		/// <summary>
+		/// Trains multiple classifiers on the emotion data set
+		/// http://lpis.csd.auth.gr/publications/tsoumakas-ismir08.pdf
+		/// The data files can be downloaded from https://downloads.sourceforge.net/project/mulan/datasets/emotions.rar
+		/// </summary>
+		/// <param name="dataFilePath"></param>
+		public static void MultiLabelMultiClassifiers(string dataFilePath)
+		{
+			var emotionData = _LoadEmotionData(dataFilePath);
+			var attributeCount = emotionData.ColumnCount - CLASSIFICATION_COUNT;
+			var attributeColumns = Enumerable.Range(0, attributeCount).ToList();
+			var classificationColumns = Enumerable.Range(emotionData.ColumnCount - CLASSIFICATION_COUNT, CLASSIFICATION_COUNT).ToList();
+			var classificationLabel = new[] {
+				"amazed-suprised",
+				"happy-pleased",
+				"relaxing-calm",
+				"quiet-still",
+				"sad-lonely",
+				"angry-aggresive"
+			};
+
+			// create six separate datasets to train, each with a separate classification column
+			var dataSets = Enumerable.Range(attributeCount, CLASSIFICATION_COUNT).Select(targetIndex => {
+				var dataTableBuider = BrightWireProvider.CreateDataTableBuilder();
+				for (var i = 0; i < attributeCount; i++)
+					dataTableBuider.AddColumn(ColumnType.Float);
+				dataTableBuider.AddColumn(ColumnType.Float, "", true);
+
+				return emotionData.Project(row => row.GetFields<float>(attributeColumns)
+					.Concat(new[] { row.GetField<float>(targetIndex) })
+					.Cast<object>()
+					.ToList()
+				);
+			}).Select(ds => ds.Split(0)).ToList();
+
+			// train classifiers on each training set
+			using (var lap = BrightWireProvider.CreateLinearAlgebra(false)) {
+				var graph = new GraphFactory(lap);
+
+				// binary classification rounds each output to 0 or 1 and compares each output against the binary classification targets
+				var errorMetric = graph.ErrorMetric.BinaryClassification;
+
+				// configure the network properties
+				graph.CurrentPropertySet
+					.Use(graph.GradientDescent.Adam)
+					.Use(graph.WeightInitialisation.Xavier)
+				;
+
+				for (var i = 0; i < CLASSIFICATION_COUNT; i++) {
+					var trainingSet = dataSets[i].Training;
+					var testSet = dataSets[i].Test;
+					Console.WriteLine("Training on {0}", classificationLabel[i]);
+
+					// train and evaluate a naive bayes classifier
+					var naiveBayes = trainingSet.TrainNaiveBayes().CreateClassifier();
+					Console.WriteLine("\tNaive bayes accuracy: {0:P}", testSet
+						.Classify(naiveBayes)
+						.Average(d => d.Row.GetField<string>(attributeCount) == d.Classification ? 1.0 : 0.0)
+					);
+
+					// train a logistic regression classifier
+					var logisticRegression = trainingSet
+						.TrainLogisticRegression(lap, 2500, 0.25f, 0.01f)
+						.CreatePredictor(lap)
+						.ConvertToRowClassifier(attributeColumns)
+					;
+					Console.WriteLine("\tLogistic regression accuracy: {0:P}", testSet
+						.Classify(logisticRegression)
+						.Average(d => d.Row.GetField<string>(attributeCount) == d.Classification ? 1.0 : 0.0)
+					);
+
+					// train and evaluate k nearest neighbours
+					var knn = trainingSet.TrainKNearestNeighbours().CreateClassifier(lap, 10);
+					Console.WriteLine("\tK nearest neighbours accuracy: {0:P}", testSet
+						.Classify(knn)
+						.Average(d => d.Row.GetField<string>(attributeCount) == d.Classification ? 1.0 : 0.0)
+					);
+
+					// create a training engine
+					const float TRAINING_RATE = 0.1f;
+					var trainingData = graph.CreateDataSource(trainingSet);
+					var testData = trainingData.CloneWith(testSet);
+					var engine = graph.CreateTrainingEngine(trainingData, TRAINING_RATE, 64);
+
+					// build the network
+					const int HIDDEN_LAYER_SIZE = 64, TRAINING_ITERATIONS = 2000;
+					var network = graph.Connect(engine)
+						.AddFeedForward(HIDDEN_LAYER_SIZE)
+						.Add(graph.SigmoidActivation())
+						.AddDropOut(dropOutPercentage: 0.5f)
+						.AddFeedForward(engine.DataSource.OutputSize)
+						.Add(graph.SigmoidActivation())
+						.AddBackpropagation(errorMetric)
+					;
+
+					// train the network
+					engine.Train(TRAINING_ITERATIONS, testData, errorMetric, null, 200);
+				}
+			}
+		}
+	}
+}

SampleCode/Program.cs

@@ -93,6 +93,8 @@ static void Main(string[] args)
 			//SimpleLinearTest();
 			//PredictBicyclesWithLinearModel(@"D:\data\bikesharing\hour.csv");
 			//PredictBicyclesWithNeuralNetwork(@"D:\data\bikesharing\hour.csv");
+			//MultiLabelSingleClassifier(@"d:\data\emotions\emotions.arff");
+			//MultiLabelMultiClassifiers(@"d:\data\emotions\emotions.arff");
 		}
     }
 }

SampleCode/SampleCode.csproj

@@ -64,6 +64,7 @@
     <Compile Include="IrisClassification.cs" />
     <Compile Include="IrisClustering.cs" />
     <Compile Include="MarkovChains.cs" />
+    <Compile Include="MultiLabel.cs" />
     <Compile Include="Program.cs" />
     <Compile Include="Properties\AssemblyInfo.cs" />
     <Compile Include="MNIST.cs" />