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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionProblemData.cs @ 6672

Last change on this file since 6672 was 6672, checked in by mkommend, 11 years ago

#1592: Implemented new estimated class values view for ClassificationEnsembleSolutions.

File size: 6.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.IO;
25using System.Linq;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Data;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31
32namespace HeuristicLab.Problems.DataAnalysis {
33  [StorableClass]
34  [Item("RegressionProblemData", "Represents an item containing all data defining a regression problem.")]
35  public class RegressionProblemData : DataAnalysisProblemData, IRegressionProblemData {
36    protected const string TargetVariableParameterName = "TargetVariable";
37
38    #region default data
39    private static double[,] kozaF1 = new double[,] {
40          {2.017885919, -1.449165046},
41          {1.30060506,  -1.344523885},
42          {1.147134798, -1.317989331},
43          {0.877182504, -1.266142284},
44          {0.852562452, -1.261020794},
45          {0.431095788, -1.158793317},
46          {0.112586002, -1.050908405},
47          {0.04594507,  -1.021989402},
48          {0.042572879, -1.020438113},
49          {-0.074027291,  -0.959859562},
50          {-0.109178553,  -0.938094706},
51          {-0.259721109,  -0.803635355},
52          {-0.272991057,  -0.387519561},
53          {-0.161978191,  -0.193611001},
54          {-0.102489983,  -0.114215349},
55          {-0.01469968, -0.014918985},
56          {-0.008863365,  -0.008942626},
57          {0.026751057, 0.026054094},
58          {0.166922436, 0.14309643},
59          {0.176953808, 0.1504144},
60          {0.190233418, 0.159916534},
61          {0.199800708, 0.166635331},
62          {0.261502822, 0.207600348},
63          {0.30182879,  0.232370249},
64          {0.83763905,  0.468046718}
65    };
66    private static readonly Dataset defaultDataset;
67    private static readonly IEnumerable<string> defaultAllowedInputVariables;
68    private static readonly string defaultTargetVariable;
69
70    private static readonly RegressionProblemData emptyProblemData;
71    public static RegressionProblemData EmptyProblemData {
72      get { return emptyProblemData; }
73    }
74
75    static RegressionProblemData() {
76      defaultDataset = new Dataset(new string[] { "y", "x" }, kozaF1);
77      defaultDataset.Name = "Fourth-order Polynomial Function Benchmark Dataset";
78      defaultDataset.Description = "f(x) = x^4 + x^3 + x^2 + x^1";
79      defaultAllowedInputVariables = new List<string>() { "x" };
80      defaultTargetVariable = "y";
81
82      var problemData = new RegressionProblemData();
83      problemData.Parameters.Clear();
84      problemData.Name = "Empty Regression ProblemData";
85      problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
86      problemData.isEmpty = true;
87
88      problemData.Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", new Dataset()));
89      problemData.Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, ""));
90      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
91      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
92      problemData.Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>()));
93      emptyProblemData = problemData;
94    }
95    #endregion
96
97    public ConstrainedValueParameter<StringValue> TargetVariableParameter {
98      get { return (ConstrainedValueParameter<StringValue>)Parameters[TargetVariableParameterName]; }
99    }
100    public string TargetVariable {
101      get { return TargetVariableParameter.Value.Value; }
102    }
103
104    [StorableConstructor]
105    protected RegressionProblemData(bool deserializing) : base(deserializing) { }
106    [StorableHook(HookType.AfterDeserialization)]
107    private void AfterDeserialization() {
108      RegisterParameterEvents();
109    }
110
111    protected RegressionProblemData(RegressionProblemData original, Cloner cloner)
112      : base(original, cloner) {
113      RegisterParameterEvents();
114    }
115    public override IDeepCloneable Clone(Cloner cloner) {
116      if (this == emptyProblemData) return emptyProblemData;
117      return new RegressionProblemData(this, cloner);
118    }
119
120    public RegressionProblemData()
121      : this(defaultDataset, defaultAllowedInputVariables, defaultTargetVariable) {
122    }
123
124    public RegressionProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable)
125      : base(dataset, allowedInputVariables) {
126      var variables = InputVariables.Select(x => x.AsReadOnly()).ToList();
127      Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>(variables), variables.Where(x => x.Value == targetVariable).First()));
128      RegisterParameterEvents();
129    }
130
131    private void RegisterParameterEvents() {
132      TargetVariableParameter.ValueChanged += new EventHandler(TargetVariableParameter_ValueChanged);
133    }
134    private void TargetVariableParameter_ValueChanged(object sender, EventArgs e) {
135      OnChanged();
136    }
137
138    #region Import from file
139    public static RegressionProblemData ImportFromFile(string fileName) {
140      TableFileParser csvFileParser = new TableFileParser();
141      csvFileParser.Parse(fileName);
142
143      Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
144      dataset.Name = Path.GetFileName(fileName);
145
146      RegressionProblemData problemData = new RegressionProblemData(dataset, dataset.VariableNames.Skip(1), dataset.VariableNames.First());
147      problemData.Name = "Data imported from " + Path.GetFileName(fileName);
148      return problemData;
149    }
150    #endregion
151  }
152}
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