source: branches/DataPreprocessing/HeuristicLab.DataPreprocessing/3.3/Implementations/PreprocessingData.cs @ 10548

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2013 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.DataPreprocessing {
  [Item("PreprocessingData", "Represents data used for preprocessing.")]
  public class PreprocessingData : NamedItem, IPreprocessingData {

    private IDictionary<int, IList> variableValues;

    private IList<string> variableNames;

    private double trainingToTestRatio;

    private PreprocessingData(PreprocessingData original, Cloner cloner)
      : base(original, cloner) {
      variableValues = new Dictionary<int, IList>(original.variableValues);
      for (int i = 0; i < variableValues.Count; i++)
        variableValues[i] = new ArrayList(original.variableValues[i]);
      variableNames = new List<string>(original.variableNames);
      trainingToTestRatio = original.trainingToTestRatio;
    }

    public PreprocessingData(IDataAnalysisProblemData problemData)
      : base() {
      Name = "-";

      variableNames = new List<string>(problemData.Dataset.VariableNames);
      // create dictionary from variable name to index

      int columnIndex = 0;
      variableValues = new Dictionary<int, IList>();
      foreach (var variableName in problemData.Dataset.VariableNames) {
        if (problemData.Dataset.IsType<double>(variableName)) {
          variableValues[columnIndex] = problemData.Dataset.GetDoubleValues(variableName).ToList();
        } else if (problemData.Dataset.IsType<string>(variableName)) {
          variableValues[columnIndex] = CreateColumn<string>(problemData.Dataset, columnIndex, x => x);
        } else if (problemData.Dataset.IsType<DateTime>(variableName)) {
          variableValues[columnIndex] = CreateColumn<DateTime>(problemData.Dataset, columnIndex, x => DateTime.Parse(x));
        } else {
          throw new ArgumentException("The datatype of column " + variableName + " must be of type List<double>, List<string> or List<DateTime>");
        }
        ++columnIndex;
      }

      trainingToTestRatio = (double)problemData.TrainingPartition.Size / Math.Max(problemData.Dataset.Rows, double.Epsilon);
    }

    private static IList CreateColumn<T>(Dataset ds, int column, Func<string, T> selector) {
      var list = new List<T>(ds.Rows);
      for (int row = 0; row < ds.Rows; ++row) {
        list.Add(selector(ds.GetValue(row, column)));
      }
      return list;
    }

    #region NamedItem abstract Member Implementations

    public override IDeepCloneable Clone(Cloner cloner) {
      return new PreprocessingData(this, cloner);
    }

    #endregion

    #region IPreprocessingData Members

    public T GetCell<T>(int columnIndex, int rowIndex) {
      return (T)variableValues[columnIndex][rowIndex];
    }


    public void SetCell<T>(int columnIndex, int rowIndex, T value) {    // Undo-sensitive
      variableValues[columnIndex][rowIndex] = value;
      OnChanged(DataPreprocessingChangedEventType.ChangeItem, columnIndex, rowIndex);
    }


    public string GetCellAsString(int columnIndex, int rowIndex) {
      return variableValues[columnIndex][rowIndex].ToString();
    }


    [Obsolete("use the index based variant, is faster")]
    public IList<T> GetValues<T>(string variableName) {
      return GetValues<T>(GetColumnIndex(variableName));
    }

    public IList<T> GetValues<T>(int columnIndex) {
      return (IList<T>)variableValues[columnIndex];
    }

    public void SetValues<T>(int columnIndex, IList<T> values) {    // Undo-sensitive
      if (IsType<T>(columnIndex)) {
        variableValues[columnIndex] = (IList)values;
      } else {
        throw new ArgumentException("The datatype of column " + columnIndex + " must be of type " + variableValues[columnIndex].GetType().Name + " but was " + typeof(T).Name);
      }
      OnChanged(DataPreprocessingChangedEventType.ChangeColumn, columnIndex, -1);
    }

    public void InsertRow(int rowIndex) {    // Undo-sensitive
      foreach (IList column in variableValues.Values) {
        Type type = column.GetType().GetGenericArguments()[0];
        column.Insert(rowIndex, type.IsValueType ? Activator.CreateInstance(type) : null);
      }
      OnChanged(DataPreprocessingChangedEventType.AddRow, -1, rowIndex);
    }

    public void DeleteRow(int rowIndex) {    // Undo-sensitive
      foreach (IList column in variableValues.Values) {
        column.RemoveAt(rowIndex);
      }
      OnChanged(DataPreprocessingChangedEventType.DeleteRow, -1, rowIndex);
    }

    public void InsertColumn<T>(string variableName, int columnIndex) {    // Undo-sensitive
      variableValues.Add(columnIndex, new List<T>(Rows));
      variableNames.Insert(columnIndex, variableName);
      OnChanged(DataPreprocessingChangedEventType.AddColumn, columnIndex, -1);
    }

    public void DeleteColumn(int columnIndex) {    // Undo-sensitive
      variableValues.Remove(columnIndex);
      variableNames.RemoveAt(columnIndex);
      OnChanged(DataPreprocessingChangedEventType.DeleteColumn, columnIndex, -1);
    }


    public IntRange TrainingPartition {
      get { return new IntRange(0, (int)(Rows * trainingToTestRatio)); }
    }

    public IntRange TestPartition {
      get { return new IntRange((int)(Rows * trainingToTestRatio), Rows); }
    }

    public string GetVariableName(int columnIndex) {
      return variableNames[columnIndex];
    }

    public IEnumerable<string> VariableNames {
      get { return variableNames; }
    }

    public int GetColumnIndex(string variableName) {
      return variableNames.IndexOf(variableName);
    }

    public bool IsType<T>(int columnIndex) {
      return variableValues[columnIndex] is List<T>;
    }

    public int Columns {
      get { return variableNames.Count; }
    }

    public int Rows {
      get { return variableValues.Count > 0 ? variableValues[0].Count : 0; }
    }

    public Dataset ExportToDataset() {
      IList<IList> values = new List<IList>();

      for (int i = 0; i < Columns; ++i) {
        values.Add(variableValues[i]);
      }

      var dataset = new Dataset(variableNames, values);
      return dataset;
    }

    public event DataPreprocessingChangedEventHandler Changed;
    protected virtual void OnChanged(DataPreprocessingChangedEventType type, int column, int row) {
      var listeners = Changed;
      if (listeners != null) listeners(this, new DataPreprocessingChangedEventArgs(type, column, row));
    }

    public bool IsUndoAvailable {
      get { throw new NotImplementedException(); }
    }

    public void Undo() {
      throw new NotImplementedException();
    }

    #endregion
  }
}