#region License Information /* HeuristicLab * Copyright (C) 2002-2018 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.Common; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.DataPreprocessing { public class ProblemDataCreator { private readonly PreprocessingContext context; private Dataset ExportedDataset { get { return context.Data.ExportToDataset(); } } private IList Transformations { get { return context.Data.Transformations; } } public ProblemDataCreator(PreprocessingContext context) { this.context = context; } public IDataAnalysisProblemData CreateProblemData(IDataAnalysisProblemData oldProblemData) { if (context.Data.Rows == 0 || context.Data.Columns == 0) return null; IDataAnalysisProblemData problemData; if (oldProblemData is TimeSeriesPrognosisProblemData) { problemData = CreateTimeSeriesPrognosisData((TimeSeriesPrognosisProblemData)oldProblemData); } else if (oldProblemData is RegressionProblemData) { problemData = CreateRegressionData((RegressionProblemData)oldProblemData); } else if (oldProblemData is ClassificationProblemData) { problemData = CreateClassificationData((ClassificationProblemData)oldProblemData); } else if (oldProblemData is ClusteringProblemData) { problemData = CreateClusteringData((ClusteringProblemData)oldProblemData); } else { throw new NotImplementedException("The type of the DataAnalysisProblemData is not supported."); } SetTrainingAndTestPartition(problemData); // set the input variables to the correct checked state var inputVariables = oldProblemData.InputVariables.ToDictionary(x => x.Value, x => x); foreach (var variable in problemData.InputVariables) { bool isChecked = inputVariables.ContainsKey(variable.Value) && oldProblemData.InputVariables.ItemChecked(inputVariables[variable.Value]); problemData.InputVariables.SetItemCheckedState(variable, isChecked); } return problemData; } private IDataAnalysisProblemData CreateTimeSeriesPrognosisData(TimeSeriesPrognosisProblemData oldProblemData) { var targetVariable = oldProblemData.TargetVariable; if (!context.Data.VariableNames.Contains(targetVariable)) targetVariable = context.Data.VariableNames.First(); var inputVariables = GetDoubleInputVariables(targetVariable); var newProblemData = new TimeSeriesPrognosisProblemData(ExportedDataset, inputVariables, targetVariable, Transformations) { TrainingHorizon = oldProblemData.TrainingHorizon, TestHorizon = oldProblemData.TestHorizon }; return newProblemData; } private IDataAnalysisProblemData CreateRegressionData(RegressionProblemData oldProblemData) { var targetVariable = oldProblemData.TargetVariable; if (!context.Data.VariableNames.Contains(targetVariable)) targetVariable = context.Data.VariableNames.First(); var inputVariables = GetDoubleInputVariables(targetVariable); var newProblemData = new RegressionProblemData(ExportedDataset, inputVariables, targetVariable, Transformations); return newProblemData; } private IDataAnalysisProblemData CreateClassificationData(ClassificationProblemData oldProblemData) { var targetVariable = oldProblemData.TargetVariable; if (!context.Data.VariableNames.Contains(targetVariable)) targetVariable = context.Data.VariableNames.First(); var inputVariables = GetDoubleInputVariables(targetVariable); var newProblemData = new ClassificationProblemData(ExportedDataset, inputVariables, targetVariable, Transformations) { PositiveClass = oldProblemData.PositiveClass }; return newProblemData; } private IDataAnalysisProblemData CreateClusteringData(ClusteringProblemData oldProblemData) { return new ClusteringProblemData(ExportedDataset, GetDoubleInputVariables(String.Empty), Transformations); } private void SetTrainingAndTestPartition(IDataAnalysisProblemData problemData) { var ppData = context.Data; problemData.TrainingPartition.Start = ppData.TrainingPartition.Start; problemData.TrainingPartition.End = ppData.TrainingPartition.End; problemData.TestPartition.Start = ppData.TestPartition.Start; problemData.TestPartition.End = ppData.TestPartition.End; } private IEnumerable GetDoubleInputVariables(string targetVariable) { var variableNames = new List(); for (int i = 0; i < context.Data.Columns; ++i) { var variableName = context.Data.GetVariableName(i); if (context.Data.VariableHasType(i) && variableName != targetVariable && IsNotConstantInputVariable(context.Data.GetValues(i))) { variableNames.Add(variableName); } } return variableNames; } private bool IsNotConstantInputVariable(IList list) { return context.Data.TrainingPartition.End - context.Data.TrainingPartition.Start > 1 || list.Range() > 0; } } }