/// <summary>
/// Stacking Regression Ensemble Learner.
/// Combines several models into a single ensemble model using a top or meta level model to combine the models.
/// The bottom level models generates output for the top level model using cross validation.
/// Default is 5-fold RandomCrossValidation.
/// </summary>
/// <param name="learners">Learners in the ensemble</param>
/// <param name="metaLearner">Meta learner or top level model for combining the ensemble models</param>
/// <param name="includeOriginalFeaturesForMetaLearner">True; the meta learner also receives the original features.
/// False; the meta learner only receives the output of the ensemble models as features. Default is true</param>
public RegressionStackingEnsembleLearner(
IIndexedLearner <double>[] learners,
ILearner <double> metaLearner,
bool includeOriginalFeaturesForMetaLearner = true)
: this(learners, (obs, targets) => metaLearner.Learn(obs, targets),
new RandomCrossValidation <double>(5, 42), includeOriginalFeaturesForMetaLearner)
{
}
/// <summary>
/// Stacking Classification Ensemble Learner.
/// Combines several models into a single ensemble model using a top or meta level model to combine the models.
/// The bottom level models generates output for the top level model using cross validation.
/// </summary>
/// <param name="learners">Learners in the ensemble</param>
/// <param name="metaLearner">Meta learner or top level model for combining the ensemble models</param>
/// <param name="crossValidation">Cross validation method</param>
/// <param name="includeOriginalFeaturesForMetaLearner">True; the meta learner also receives the original features.
/// False; the meta learner only receives the output of the ensemble models as features. Default is true</param>
public ClassificationStackingEnsembleLearner(
IIndexedLearner <ProbabilityPrediction>[] learners,
ILearner <ProbabilityPrediction> metaLearner,
ICrossValidation <ProbabilityPrediction> crossValidation,
bool includeOriginalFeaturesForMetaLearner = true)
: this(learners, (obs, targets) => metaLearner.Learn(obs, targets),
crossValidation, includeOriginalFeaturesForMetaLearner)
{
}
public void TrainModel()
{
var miniBatches = CreateMiniBatches(trainingInputOutputPairs, batchSize);
foreach (var batch in miniBatches)
{
learner.Learn(batch);
}
}
static void TeachSomething(ILearner learner, string lesson)
{
learner.Learn(lesson);
}
public void Teach(Lesson lesson, ILearner learner)
{
learner.Learn(lesson);
}
public void Train(string dataName, string savePath)
{
var parser = new CsvParser(() => new StreamReader(root + dataName + ".csv"), ',');
var targetName = "f5";
var enemyName = "f6";
try
{
// read data
var observations = parser.EnumerateRows(c => c != targetName && c != enemyName)
.ToF64Matrix();
var targets = parser.EnumerateRows(targetName)
.ToF64Vector();
//Debug.Log("READ");
// split data
//var splitter = new StratifiedTrainingTestIndexSplitter<double>(trainingPercentage: 0.9, seed: 1);
//var trainingTestSplit = splitter.SplitSet(observations, targets);
//var trainingSet = trainingTestSplit.TrainingSet;
//var testSet = trainingTestSplit.TestSet;
//Debug.Log("SPLIT");
//model = learner.Learn(trainingSet.Observations, trainingSet.Targets);
model = learner.Learn(observations, targets);
//Debug.Log("TRAINED");
//var met1 = new MeanSquaredErrorRegressionMetric();
//var met2 = new CoefficientOfDeterminationMetric();
//var met3 = new MeanAbsolutErrorRegressionMetric();
//var met4 = new NormalizedGiniCoefficientRegressionMetric();
//var met5 = new RootMeanSquarePercentageRegressionMetric();
//var pred = model.Predict(testSet.Observations);
//Debug.Log(met1.Error(testSet.Targets, pred) + " <-> " + type);
//Debug.Log(met2.Error(testSet.Targets, pred) + " <-> " + type);
//Debug.Log(met3.Error(testSet.Targets, pred) + " <-> " + type);
//Debug.Log(met4.Error(testSet.Targets, pred) + " <-> " + type);
//Debug.Log(met5.Error(testSet.Targets, pred) + " <-> " + type);
// NEURAL TEST
Debug.Log("TRAINED");
// save
SaveModel(savePath);
// load
//LoadModel("" + LevelData.instance.stage + "/" + LevelData.instance.playerID + ".model");
}
catch (Exception e)
{
Debug.Log(e.Message);
model = null;
}
finally
{
}
}