#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; using System.Collections.Generic; using System.Linq; using HeuristicLab.Collections; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis { [StorableClass] public abstract class DataAnalysisProblemData : ParameterizedNamedItem, IDataAnalysisProblemData { protected const string DatasetParameterName = "Dataset"; protected const string InputVariablesParameterName = "InputVariables"; protected const string TrainingPartitionParameterName = "TrainingPartition"; protected const string TestPartitionParameterName = "TestPartition"; #region parameter properites public IFixedValueParameter DatasetParameter { get { return (IFixedValueParameter)Parameters[DatasetParameterName]; } } public IFixedValueParameter> InputVariablesParameter { get { return (IFixedValueParameter>)Parameters[InputVariablesParameterName]; } } public IFixedValueParameter TrainingPartitionParameter { get { return (IFixedValueParameter)Parameters[TrainingPartitionParameterName]; } } public IFixedValueParameter TestPartitionParameter { get { return (IFixedValueParameter)Parameters[TestPartitionParameterName]; } } #endregion #region properties protected bool isEmpty = false; public bool IsEmpty { get { return isEmpty; } } public Dataset Dataset { get { return DatasetParameter.Value; } } public ICheckedItemList InputVariables { get { return InputVariablesParameter.Value; } } public IEnumerable AllowedInputVariables { get { return InputVariables.CheckedItems.Select(x => x.Value.Value); } } public IntRange TrainingPartition { get { return TrainingPartitionParameter.Value; } } public IntRange TestPartition { get { return TestPartitionParameter.Value; } } public virtual IEnumerable TrainingIndizes { get { return Enumerable.Range(TrainingPartition.Start, TrainingPartition.End - TrainingPartition.Start) .Where(IsTrainingSample); } } public virtual IEnumerable TestIndizes { get { return Enumerable.Range(TestPartition.Start, TestPartition.End - TestPartition.Start) .Where(IsTestSample); } } public virtual bool IsTrainingSample(int index) { return index >= 0 && index < Dataset.Rows && TrainingPartition.Start <= index && index < TrainingPartition.End && (index < TestPartition.Start || TestPartition.End <= index); } public virtual bool IsTestSample(int index) { return index >= 0 && index < Dataset.Rows && TestPartition.Start <= index && index < TestPartition.End; } #endregion protected DataAnalysisProblemData(DataAnalysisProblemData original, Cloner cloner) : base(original, cloner) { isEmpty = original.isEmpty; RegisterEventHandlers(); } [StorableConstructor] protected DataAnalysisProblemData(bool deserializing) : base(deserializing) { } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { RegisterEventHandlers(); } protected DataAnalysisProblemData(Dataset dataset, IEnumerable allowedInputVariables) { if (dataset == null) throw new ArgumentNullException("The dataset must not be null."); if (allowedInputVariables == null) throw new ArgumentNullException("The allowedInputVariables must not be null."); if (allowedInputVariables.Except(dataset.DoubleVariables).Any()) throw new ArgumentException("All allowed input variables must be present in the dataset and of type double."); var inputVariables = new CheckedItemList(dataset.DoubleVariables.Select(x => new StringValue(x))); foreach (StringValue x in inputVariables) inputVariables.SetItemCheckedState(x, allowedInputVariables.Contains(x.Value)); int trainingPartitionStart = 0; int trainingPartitionEnd = dataset.Rows / 2; int testPartitionStart = dataset.Rows / 2; int testPartitionEnd = dataset.Rows; Parameters.Add(new FixedValueParameter(DatasetParameterName, "", dataset)); Parameters.Add(new FixedValueParameter>(InputVariablesParameterName, "", inputVariables.AsReadOnly())); Parameters.Add(new FixedValueParameter(TrainingPartitionParameterName, "", new IntRange(trainingPartitionStart, trainingPartitionEnd))); Parameters.Add(new FixedValueParameter(TestPartitionParameterName, "", new IntRange(testPartitionStart, testPartitionEnd))); ((ValueParameter)DatasetParameter).ReactOnValueToStringChangedAndValueItemImageChanged = false; RegisterEventHandlers(); } private void RegisterEventHandlers() { DatasetParameter.ValueChanged += new EventHandler(Parameter_ValueChanged); InputVariables.CheckedItemsChanged += new CollectionItemsChangedEventHandler>(InputVariables_CheckedItemsChanged); TrainingPartition.ValueChanged += new EventHandler(Parameter_ValueChanged); TestPartition.ValueChanged += new EventHandler(Parameter_ValueChanged); } private void InputVariables_CheckedItemsChanged(object sender, CollectionItemsChangedEventArgs> e) { OnChanged(); } private void Parameter_ValueChanged(object sender, EventArgs e) { OnChanged(); } public event EventHandler Changed; protected virtual void OnChanged() { var listeners = Changed; if (listeners != null) listeners(this, EventArgs.Empty); } } }