#region License Information /* HeuristicLab * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using System; using HeuristicLab.Data; namespace HeuristicLab.Problems.DataAnalysis { /// /// Represents regression solutions that contain an ensemble of multiple regression models /// [StorableClass] [Item("Regression Ensemble Solution", "A regression solution that contains an ensemble of multiple regression models")] // [Creatable("Data Analysis")] public class RegressionEnsembleSolution : RegressionSolution, IRegressionEnsembleSolution { public new IRegressionEnsembleModel Model { get { return (IRegressionEnsembleModel)base.Model; } } [Storable] private Dictionary trainingPartitions; [Storable] private Dictionary testPartitions; [StorableConstructor] protected RegressionEnsembleSolution(bool deserializing) : base(deserializing) { } protected RegressionEnsembleSolution(RegressionEnsembleSolution original, Cloner cloner) : base(original, cloner) { } public RegressionEnsembleSolution(IEnumerable models, IRegressionProblemData problemData) : base(new RegressionEnsembleModel(models), problemData) { trainingPartitions = new Dictionary(); testPartitions = new Dictionary(); foreach (var model in models) { trainingPartitions[model] = (IntRange)problemData.TrainingPartition.Clone(); testPartitions[model] = (IntRange)problemData.TestPartition.Clone(); } RecalculateResults(); } public RegressionEnsembleSolution(IEnumerable models, IRegressionProblemData problemData, IEnumerable trainingPartitions, IEnumerable testPartitions) : base(new RegressionEnsembleModel(models), problemData) { this.trainingPartitions = new Dictionary(); this.testPartitions = new Dictionary(); var modelEnumerator = models.GetEnumerator(); var trainingPartitionEnumerator = trainingPartitions.GetEnumerator(); var testPartitionEnumerator = testPartitions.GetEnumerator(); while (modelEnumerator.MoveNext() & trainingPartitionEnumerator.MoveNext() & testPartitionEnumerator.MoveNext()) { this.trainingPartitions[modelEnumerator.Current] = (IntRange)trainingPartitionEnumerator.Current.Clone(); this.testPartitions[modelEnumerator.Current] = (IntRange)testPartitionEnumerator.Current.Clone(); } if (modelEnumerator.MoveNext() | trainingPartitionEnumerator.MoveNext() | testPartitionEnumerator.MoveNext()) { throw new ArgumentException(); } } public override IDeepCloneable Clone(Cloner cloner) { return new RegressionEnsembleSolution(this, cloner); } public override IEnumerable EstimatedTrainingValues { get { var estimatedValuesEnumerators = (from model in Model.Models select new { Model = model, EstimatedValuesEnumerator = model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndizes).GetEnumerator() }) .ToList(); var rowsEnumerator = ProblemData.TestIndizes.GetEnumerator(); while (rowsEnumerator.MoveNext() & estimatedValuesEnumerators.All(en => en.EstimatedValuesEnumerator.MoveNext())) { int currentRow = rowsEnumerator.Current; var selectedEnumerators = from pair in estimatedValuesEnumerators where trainingPartitions == null || !trainingPartitions.ContainsKey(pair.Model) || (trainingPartitions[pair.Model].Start >= currentRow && trainingPartitions[pair.Model].End < currentRow) select pair.EstimatedValuesEnumerator; yield return AggregateEstimatedValues(selectedEnumerators.Select(x => x.Current)); } } } public override IEnumerable EstimatedTestValues { get { var estimatedValuesEnumerators = (from model in Model.Models select new { Model = model, EstimatedValuesEnumerator = model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndizes).GetEnumerator() }) .ToList(); var rowsEnumerator = ProblemData.TestIndizes.GetEnumerator(); while (rowsEnumerator.MoveNext() & estimatedValuesEnumerators.All(en => en.EstimatedValuesEnumerator.MoveNext())) { int currentRow = rowsEnumerator.Current; var selectedEnumerators = from pair in estimatedValuesEnumerators where testPartitions == null || !testPartitions.ContainsKey(pair.Model) || (testPartitions[pair.Model].Start >= currentRow && testPartitions[pair.Model].End < currentRow) select pair.EstimatedValuesEnumerator; yield return AggregateEstimatedValues(selectedEnumerators.Select(x => x.Current)); } } } public override IEnumerable GetEstimatedValues(IEnumerable rows) { return from xs in GetEstimatedValueVectors(ProblemData.Dataset, rows) select AggregateEstimatedValues(xs); } public IEnumerable> GetEstimatedValueVectors(Dataset dataset, IEnumerable rows) { var estimatedValuesEnumerators = (from model in Model.Models select model.GetEstimatedValues(dataset, rows).GetEnumerator()) .ToList(); while (estimatedValuesEnumerators.All(en => en.MoveNext())) { yield return from enumerator in estimatedValuesEnumerators select enumerator.Current; } } private double AggregateEstimatedValues(IEnumerable estimatedValues) { return estimatedValues.Average(); } //[Storable] //private string name; //public string Name { // get { // return name; // } // set { // if (value != null && value != name) { // var cancelEventArgs = new CancelEventArgs(value); // OnNameChanging(cancelEventArgs); // if (cancelEventArgs.Cancel == false) { // name = value; // OnNamedChanged(EventArgs.Empty); // } // } // } //} //public bool CanChangeName { // get { return true; } //} //[Storable] //private string description; //public string Description { // get { // return description; // } // set { // if (value != null && value != description) { // description = value; // OnDescriptionChanged(EventArgs.Empty); // } // } //} //public bool CanChangeDescription { // get { return true; } //} //#region events //public event EventHandler> NameChanging; //private void OnNameChanging(CancelEventArgs cancelEventArgs) { // var listener = NameChanging; // if (listener != null) listener(this, cancelEventArgs); //} //public event EventHandler NameChanged; //private void OnNamedChanged(EventArgs e) { // var listener = NameChanged; // if (listener != null) listener(this, e); //} //public event EventHandler DescriptionChanged; //private void OnDescriptionChanged(EventArgs e) { // var listener = DescriptionChanged; // if (listener != null) listener(this, e); //} // #endregion } }