1 | #region License Information
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2 |
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3 | /* HeuristicLab
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4 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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5 | *
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6 | * This file is part of HeuristicLab.
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7 | *
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8 | * HeuristicLab is free software: you can redistribute it and/or modify
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9 | * it under the terms of the GNU General Public License as published by
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10 | * the Free Software Foundation, either version 3 of the License, or
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11 | * (at your option) any later version.
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12 | *
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13 | * HeuristicLab is distributed in the hope that it will be useful,
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14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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16 | * GNU General Public License for more details.
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17 | *
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18 | * You should have received a copy of the GNU General Public License
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19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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20 | */
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21 |
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22 | #endregion
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23 |
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24 | using System;
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25 | using System.Collections.Generic;
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26 | using System.Linq;
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27 | using HeuristicLab.Common;
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28 | using HeuristicLab.Core;
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29 | using HeuristicLab.Data;
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30 | using HeuristicLab.Parameters;
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31 | using HeuristicLab.Persistence;
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32 | using HeuristicLab.Random;
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33 |
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34 | namespace HeuristicLab.Problems.DataAnalysis {
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35 | [StorableType("de95788b-0353-4996-b307-c66432460ed2")]
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36 | [Item("RegressionSolution Impacts Calculator", "Calculation of the impacts of input variables for any regression solution")]
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37 | public sealed class RegressionSolutionVariableImpactsCalculator : ParameterizedNamedItem {
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38 | public enum ReplacementMethodEnum {
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39 | Median,
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40 | Average,
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41 | Shuffle,
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42 | Noise
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43 | }
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44 | public enum FactorReplacementMethodEnum {
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45 | Best,
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46 | Mode,
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47 | Shuffle
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48 | }
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49 | public enum DataPartitionEnum {
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50 | Training,
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51 | Test,
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52 | All
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53 | }
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54 |
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55 | private const string ReplacementParameterName = "Replacement Method";
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56 | private const string DataPartitionParameterName = "DataPartition";
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57 |
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58 | public IFixedValueParameter<EnumValue<ReplacementMethodEnum>> ReplacementParameter {
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59 | get { return (IFixedValueParameter<EnumValue<ReplacementMethodEnum>>)Parameters[ReplacementParameterName]; }
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60 | }
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61 | public IFixedValueParameter<EnumValue<DataPartitionEnum>> DataPartitionParameter {
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62 | get { return (IFixedValueParameter<EnumValue<DataPartitionEnum>>)Parameters[DataPartitionParameterName]; }
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63 | }
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64 |
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65 | public ReplacementMethodEnum ReplacementMethod {
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66 | get { return ReplacementParameter.Value.Value; }
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67 | set { ReplacementParameter.Value.Value = value; }
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68 | }
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69 | public DataPartitionEnum DataPartition {
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70 | get { return DataPartitionParameter.Value.Value; }
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71 | set { DataPartitionParameter.Value.Value = value; }
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72 | }
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73 |
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74 |
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75 | [StorableConstructor]
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76 | private RegressionSolutionVariableImpactsCalculator(bool deserializing) : base(deserializing) { }
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77 | private RegressionSolutionVariableImpactsCalculator(RegressionSolutionVariableImpactsCalculator original, Cloner cloner)
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78 | : base(original, cloner) { }
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79 | public override IDeepCloneable Clone(Cloner cloner) {
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80 | return new RegressionSolutionVariableImpactsCalculator(this, cloner);
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81 | }
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82 |
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83 | public RegressionSolutionVariableImpactsCalculator()
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84 | : base() {
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85 | Parameters.Add(new FixedValueParameter<EnumValue<ReplacementMethodEnum>>(ReplacementParameterName, "The replacement method for variables during impact calculation.", new EnumValue<ReplacementMethodEnum>(ReplacementMethodEnum.Median)));
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86 | Parameters.Add(new FixedValueParameter<EnumValue<DataPartitionEnum>>(DataPartitionParameterName, "The data partition on which the impacts are calculated.", new EnumValue<DataPartitionEnum>(DataPartitionEnum.Training)));
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87 | }
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88 |
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89 | //mkommend: annoying name clash with static method, open to better naming suggestions
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90 | public IEnumerable<Tuple<string, double>> Calculate(IRegressionSolution solution) {
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91 | return CalculateImpacts(solution, DataPartition, ReplacementMethod);
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92 | }
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93 |
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94 | public static IEnumerable<Tuple<string, double>> CalculateImpacts(
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95 | IRegressionSolution solution,
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96 | DataPartitionEnum data = DataPartitionEnum.Training,
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97 | ReplacementMethodEnum replacementMethod = ReplacementMethodEnum.Median,
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98 | FactorReplacementMethodEnum factorReplacementMethod = FactorReplacementMethodEnum.Best) {
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99 |
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100 | var problemData = solution.ProblemData;
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101 | var dataset = problemData.Dataset;
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102 |
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103 | IEnumerable<int> rows;
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104 | IEnumerable<double> targetValues;
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105 | double originalR2 = -1;
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106 |
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107 | OnlineCalculatorError error;
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108 |
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109 | switch (data) {
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110 | case DataPartitionEnum.All:
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111 | rows = solution.ProblemData.AllIndices;
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112 | targetValues = problemData.TargetVariableValues.ToList();
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113 | originalR2 = OnlinePearsonsRCalculator.Calculate(problemData.TargetVariableValues, solution.EstimatedValues, out error);
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114 | if (error != OnlineCalculatorError.None) throw new InvalidOperationException("Error during R² calculation.");
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115 | originalR2 = originalR2 * originalR2;
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116 | break;
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117 | case DataPartitionEnum.Training:
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118 | rows = problemData.TrainingIndices;
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119 | targetValues = problemData.TargetVariableTrainingValues.ToList();
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120 | originalR2 = solution.TrainingRSquared;
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121 | break;
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122 | case DataPartitionEnum.Test:
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123 | rows = problemData.TestIndices;
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124 | targetValues = problemData.TargetVariableTestValues.ToList();
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125 | originalR2 = solution.TestRSquared;
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126 | break;
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127 | default: throw new ArgumentException(string.Format("DataPartition {0} cannot be handled.", data));
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128 | }
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129 |
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130 | var impacts = new Dictionary<string, double>();
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131 | var modifiableDataset = ((Dataset)dataset).ToModifiable();
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132 |
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133 | var inputvariables = new HashSet<string>(problemData.AllowedInputVariables.Union(solution.Model.VariablesUsedForPrediction));
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134 | var allowedInputVariables = dataset.VariableNames.Where(v => inputvariables.Contains(v)).ToList();
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135 |
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136 | // calculate impacts for double variables
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137 | foreach (var inputVariable in allowedInputVariables.Where(problemData.Dataset.VariableHasType<double>)) {
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138 | var newEstimates = EvaluateModelWithReplacedVariable(solution.Model, inputVariable, modifiableDataset, rows, replacementMethod);
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139 | var newR2 = OnlinePearsonsRCalculator.Calculate(targetValues, newEstimates, out error);
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140 | if (error != OnlineCalculatorError.None) throw new InvalidOperationException("Error during R² calculation with replaced inputs.");
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141 |
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142 | newR2 = newR2 * newR2;
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143 | var impact = originalR2 - newR2;
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144 | impacts[inputVariable] = impact;
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145 | }
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146 |
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147 | // calculate impacts for string variables
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148 | foreach (var inputVariable in allowedInputVariables.Where(problemData.Dataset.VariableHasType<string>)) {
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149 | if (factorReplacementMethod == FactorReplacementMethodEnum.Best) {
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150 | // try replacing with all possible values and find the best replacement value
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151 | var smallestImpact = double.PositiveInfinity;
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152 | foreach (var repl in problemData.Dataset.GetStringValues(inputVariable, rows).Distinct()) {
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153 | var newEstimates = EvaluateModelWithReplacedVariable(solution.Model, inputVariable, modifiableDataset, rows,
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154 | Enumerable.Repeat(repl, dataset.Rows));
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155 | var newR2 = OnlinePearsonsRCalculator.Calculate(targetValues, newEstimates, out error);
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156 | if (error != OnlineCalculatorError.None)
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157 | throw new InvalidOperationException("Error during R² calculation with replaced inputs.");
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158 |
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159 | newR2 = newR2 * newR2;
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160 | var impact = originalR2 - newR2;
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161 | if (impact < smallestImpact) smallestImpact = impact;
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162 | }
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163 | impacts[inputVariable] = smallestImpact;
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164 | } else {
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165 | // for replacement methods shuffle and mode
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166 | // calculate impacts for factor variables
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167 |
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168 | var newEstimates = EvaluateModelWithReplacedVariable(solution.Model, inputVariable, modifiableDataset, rows,
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169 | factorReplacementMethod);
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170 | var newR2 = OnlinePearsonsRCalculator.Calculate(targetValues, newEstimates, out error);
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171 | if (error != OnlineCalculatorError.None)
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172 | throw new InvalidOperationException("Error during R² calculation with replaced inputs.");
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173 |
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174 | newR2 = newR2 * newR2;
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175 | var impact = originalR2 - newR2;
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176 | impacts[inputVariable] = impact;
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177 | }
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178 | } // foreach
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179 | return impacts.OrderByDescending(i => i.Value).Select(i => Tuple.Create(i.Key, i.Value));
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180 | }
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181 |
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182 | private static IEnumerable<double> EvaluateModelWithReplacedVariable(IRegressionModel model, string variable, ModifiableDataset dataset, IEnumerable<int> rows, ReplacementMethodEnum replacement = ReplacementMethodEnum.Median) {
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183 | var originalValues = dataset.GetReadOnlyDoubleValues(variable).ToList();
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184 | double replacementValue;
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185 | List<double> replacementValues;
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186 | IRandom rand;
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187 |
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188 | switch (replacement) {
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189 | case ReplacementMethodEnum.Median:
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190 | replacementValue = rows.Select(r => originalValues[r]).Median();
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191 | replacementValues = Enumerable.Repeat(replacementValue, dataset.Rows).ToList();
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192 | break;
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193 | case ReplacementMethodEnum.Average:
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194 | replacementValue = rows.Select(r => originalValues[r]).Average();
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195 | replacementValues = Enumerable.Repeat(replacementValue, dataset.Rows).ToList();
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196 | break;
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197 | case ReplacementMethodEnum.Shuffle:
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198 | // new var has same empirical distribution but the relation to y is broken
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199 | rand = new FastRandom(31415);
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200 | // prepare a complete column for the dataset
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201 | replacementValues = Enumerable.Repeat(double.NaN, dataset.Rows).ToList();
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202 | // shuffle only the selected rows
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203 | var shuffledValues = rows.Select(r => originalValues[r]).Shuffle(rand).ToList();
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204 | int i = 0;
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205 | // update column values
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206 | foreach (var r in rows) {
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207 | replacementValues[r] = shuffledValues[i++];
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208 | }
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209 | break;
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210 | case ReplacementMethodEnum.Noise:
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211 | var avg = rows.Select(r => originalValues[r]).Average();
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212 | var stdDev = rows.Select(r => originalValues[r]).StandardDeviation();
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213 | rand = new FastRandom(31415);
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214 | // prepare a complete column for the dataset
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215 | replacementValues = Enumerable.Repeat(double.NaN, dataset.Rows).ToList();
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216 | // update column values
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217 | foreach (var r in rows) {
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218 | replacementValues[r] = NormalDistributedRandom.NextDouble(rand, avg, stdDev);
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219 | }
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220 | break;
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221 |
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222 | default:
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223 | throw new ArgumentException(string.Format("ReplacementMethod {0} cannot be handled.", replacement));
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224 | }
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225 |
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226 | return EvaluateModelWithReplacedVariable(model, variable, dataset, rows, replacementValues);
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227 | }
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228 |
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229 | private static IEnumerable<double> EvaluateModelWithReplacedVariable(
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230 | IRegressionModel model, string variable, ModifiableDataset dataset,
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231 | IEnumerable<int> rows,
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232 | FactorReplacementMethodEnum replacement = FactorReplacementMethodEnum.Shuffle) {
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233 | var originalValues = dataset.GetReadOnlyStringValues(variable).ToList();
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234 | List<string> replacementValues;
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235 | IRandom rand;
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236 |
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237 | switch (replacement) {
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238 | case FactorReplacementMethodEnum.Mode:
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239 | var mostCommonValue = rows.Select(r => originalValues[r])
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240 | .GroupBy(v => v)
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241 | .OrderByDescending(g => g.Count())
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242 | .First().Key;
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243 | replacementValues = Enumerable.Repeat(mostCommonValue, dataset.Rows).ToList();
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244 | break;
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245 | case FactorReplacementMethodEnum.Shuffle:
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246 | // new var has same empirical distribution but the relation to y is broken
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247 | rand = new FastRandom(31415);
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248 | // prepare a complete column for the dataset
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249 | replacementValues = Enumerable.Repeat(string.Empty, dataset.Rows).ToList();
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250 | // shuffle only the selected rows
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251 | var shuffledValues = rows.Select(r => originalValues[r]).Shuffle(rand).ToList();
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252 | int i = 0;
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253 | // update column values
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254 | foreach (var r in rows) {
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255 | replacementValues[r] = shuffledValues[i++];
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256 | }
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257 | break;
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258 | default:
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259 | throw new ArgumentException(string.Format("FactorReplacementMethod {0} cannot be handled.", replacement));
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260 | }
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261 |
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262 | return EvaluateModelWithReplacedVariable(model, variable, dataset, rows, replacementValues);
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263 | }
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264 |
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265 | private static IEnumerable<double> EvaluateModelWithReplacedVariable(IRegressionModel model, string variable,
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266 | ModifiableDataset dataset, IEnumerable<int> rows, IEnumerable<double> replacementValues) {
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267 | var originalValues = dataset.GetReadOnlyDoubleValues(variable).ToList();
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268 | dataset.ReplaceVariable(variable, replacementValues.ToList());
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269 | //mkommend: ToList is used on purpose to avoid lazy evaluation that could result in wrong estimates due to variable replacements
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270 | var estimates = model.GetEstimatedValues(dataset, rows).ToList();
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271 | dataset.ReplaceVariable(variable, originalValues);
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272 |
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273 | return estimates;
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274 | }
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275 | private static IEnumerable<double> EvaluateModelWithReplacedVariable(IRegressionModel model, string variable,
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276 | ModifiableDataset dataset, IEnumerable<int> rows, IEnumerable<string> replacementValues) {
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277 | var originalValues = dataset.GetReadOnlyStringValues(variable).ToList();
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278 | dataset.ReplaceVariable(variable, replacementValues.ToList());
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279 | //mkommend: ToList is used on purpose to avoid lazy evaluation that could result in wrong estimates due to variable replacements
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280 | var estimates = model.GetEstimatedValues(dataset, rows).ToList();
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281 | dataset.ReplaceVariable(variable, originalValues);
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282 |
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283 | return estimates;
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284 | }
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285 | }
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286 | }
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