public static void Run(string trainingSetPath, string testingSetPath, int numberOfModels, int?randomSeed) { Trace.TraceInformation("Starting bagging"); Trace.TraceInformation("TrainingSetPath: {0}", trainingSetPath); Trace.TraceInformation("TestingSetPath: {0}", testingSetPath); Trace.TraceInformation("Models: {0}", numberOfModels); Instances trainingInstances = Diabetes.LoadData(trainingSetPath, Mode.Train); Bagging bagging = new Bagging(); bagging.Train(trainingInstances, numberOfModels, randomSeed); Instances testingInstances = Diabetes.LoadData(testingSetPath, Mode.Test); bagging.TestNonBagging(testingInstances); bagging.Test(testingInstances); }
public static void RunNonBagging(string trainingSetPath, string testingSetPath, int maxTreeDepth) { Trace.TraceInformation("Starting Bias-Variance for NonBagging"); Trace.TraceInformation("TrainingSetPath: {0}", trainingSetPath); Trace.TraceInformation("TestingSetPath: {0}", testingSetPath); Trace.TraceInformation("Max Tree Depth: {0}", maxTreeDepth); // Train Instances trainingInstances = Diabetes.LoadData(trainingSetPath, Mode.Train); Bagging.Bagging bagging = new Bagging.Bagging(); bagging.Train(trainingInstances, 0, null, maxTreeDepth); // Predict Instances testingInstances = Diabetes.LoadData(testingSetPath, Mode.Test); List <int> predictions = bagging.TestNonBagging(testingInstances); // Calculate Bias and Variance // Get the class for each test example int targetAttribute = testingInstances.numAttributes() - 1; List <int> classes = new List <int>(); for (int i = 0; i < testingInstances.numInstances(); i++) { classes.Add((int)testingInstances.instance(i).value(targetAttribute)); } // Get the prediction for every test example, for every set of instanes List <List <int> > allPredictions = new List <List <int> >(); for (int j = 0; j < 1; j++) { for (int i = 0; i < testingInstances.numInstances(); i++) { if (j == 0) { allPredictions.Add(new List <int>()); } allPredictions[i].Add(predictions[i]); } } BiasVariance.biasvar(classes, allPredictions, testingInstances.numInstances(), 1); }
public static void RunBagging(string trainingSetPath, string testingSetPath, int numberOfModels, int bootstrapSamples, int?randomSeed, int maxTreeDepth) { Trace.TraceInformation("Starting Bias-Variance for Bagging"); Trace.TraceInformation("TrainingSetPath: {0}", trainingSetPath); Trace.TraceInformation("TestingSetPath: {0}", testingSetPath); Trace.TraceInformation("Models: {0}", numberOfModels); Trace.TraceInformation("Bootstrap Samples: {0}", bootstrapSamples); Trace.TraceInformation("Random Seed: {0}", randomSeed.ToString()); Trace.TraceInformation("Max Tree Depth: {0}", maxTreeDepth); Instances trainingInstances = Diabetes.LoadData(trainingSetPath, Mode.Train); Instances testingInstances = Diabetes.LoadData(testingSetPath, Mode.Test); // Stores the predictions for every training set List <List <int> > allPredictions = new List <List <int> >(); for (int i = 0; i < bootstrapSamples; i++) { Bagging.Bagging bagging = new Bagging.Bagging(); bagging.Train(trainingInstances, numberOfModels, randomSeed, maxTreeDepth); List <int> predictions = bagging.Test(testingInstances); for (int k = 0; k < testingInstances.numInstances(); k++) { if (i == 0) { allPredictions.Add(new List <int>()); } allPredictions[k].Add(predictions[k]); } } // Calculate Bias and Variance // Get the class for each test example int targetAttribute = testingInstances.numAttributes() - 1; List <int> classes = new List <int>(); for (int i = 0; i < testingInstances.numInstances(); i++) { classes.Add((int)testingInstances.instance(i).value(targetAttribute)); } BiasVariance.biasvar(classes, allPredictions, testingInstances.numInstances(), bootstrapSamples); }