#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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.Drawing; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.DataPreprocessing { [Item("Manipulation", "Represents the available manipulations on a data set.")] [StorableClass] public class ManipulationContent : PreprocessingContent, IViewShortcut { public static new Image StaticItemImage { get { return HeuristicLab.Common.Resources.VSImageLibrary.Method; } } #region Constructor, Cloning & Persistence public ManipulationContent(IFilteredPreprocessingData preprocessingData) : base(preprocessingData) { } public ManipulationContent(ManipulationContent original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new ManipulationContent(this, cloner); } [StorableConstructor] protected ManipulationContent(bool deserializing) : base(deserializing) { } #endregion public List RowsWithMissingValuesGreater(double percent) { List rows = new List(); for (int i = 0; i < PreprocessingData.Rows; ++i) { int missingCount = PreprocessingData.GetRowMissingValueCount(i); if (100f / PreprocessingData.Columns * missingCount > percent) { rows.Add(i); } } return rows; } public List ColumnsWithMissingValuesGreater(double percent) { List columns = new List(); for (int i = 0; i < PreprocessingData.Columns; ++i) { int missingCount = PreprocessingData.GetMissingValueCount(i); if (100f / PreprocessingData.Rows * missingCount > percent) { columns.Add(i); } } return columns; } public List ColumnsWithVarianceSmaller(double variance) { List columns = new List(); for (int i = 0; i < PreprocessingData.Columns; ++i) { if (PreprocessingData.VariableHasType(i)) { double columnVariance = PreprocessingData.GetVariance(i); if (columnVariance < variance) { columns.Add(i); } } else if (PreprocessingData.VariableHasType(i)) { double columnVariance = (double)PreprocessingData.GetVariance(i).Ticks / TimeSpan.TicksPerSecond; if (columnVariance < variance) { columns.Add(i); } } } return columns; } public void DeleteRowsWithMissingValuesGreater(double percent) { DeleteRows(RowsWithMissingValuesGreater(percent)); } public void DeleteColumnsWithMissingValuesGreater(double percent) { DeleteColumns(ColumnsWithMissingValuesGreater(percent)); } public void DeleteColumnsWithVarianceSmaller(double variance) { DeleteColumns(ColumnsWithVarianceSmaller(variance)); } private void DeleteRows(List rows) { PreprocessingData.InTransaction(() => { foreach (int row in rows.OrderByDescending(x => x)) { PreprocessingData.DeleteRow(row); } }); } private void DeleteColumns(List columns) { PreprocessingData.InTransaction(() => { foreach (int column in columns.OrderByDescending(x => x)) { PreprocessingData.DeleteColumn(column); } }); } } }