1 | // Copyright 2015 Georg-August-Universität Göttingen, Germany |
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2 | // |
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3 | // Licensed under the Apache License, Version 2.0 (the "License"); |
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4 | // you may not use this file except in compliance with the License. |
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5 | // You may obtain a copy of the License at |
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6 | // |
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7 | // http://www.apache.org/licenses/LICENSE-2.0 |
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8 | // |
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9 | // Unless required by applicable law or agreed to in writing, software |
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10 | // distributed under the License is distributed on an "AS IS" BASIS, |
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11 | // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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12 | // See the License for the specific language governing permissions and |
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13 | // limitations under the License. |
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14 | |
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15 | package de.ugoe.cs.cpdp.training; |
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16 | |
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17 | import java.util.ArrayList; |
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18 | import java.util.Arrays; |
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19 | import java.util.Collections; |
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20 | import java.util.Comparator; |
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21 | import java.util.HashMap; |
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22 | import java.util.Iterator; |
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23 | import java.util.LinkedHashMap; |
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24 | import java.util.LinkedList; |
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25 | import java.util.List; |
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26 | import java.util.Map; |
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27 | import java.util.Map.Entry; |
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28 | import java.util.logging.Level; |
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29 | |
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30 | import java.util.Random; |
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31 | |
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32 | import org.apache.commons.collections4.list.SetUniqueList; |
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33 | import org.apache.commons.math3.stat.correlation.SpearmansCorrelation; |
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34 | import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest; |
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35 | |
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36 | import de.ugoe.cs.util.console.Console; |
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37 | import weka.attributeSelection.SignificanceAttributeEval; |
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38 | import weka.classifiers.AbstractClassifier; |
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39 | import weka.classifiers.Classifier; |
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40 | import weka.core.Attribute; |
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41 | import weka.core.DenseInstance; |
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42 | import weka.core.Instance; |
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43 | import weka.core.Instances; |
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44 | |
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45 | /** |
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46 | * Implements Heterogenous Defect Prediction after Nam et al. 2015. |
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47 | * |
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48 | * We extend WekaBaseTraining because we have to Wrap the Classifier to use MetricMatching. |
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49 | * This also means we can use any Weka Classifier not just LogisticRegression. |
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50 | * |
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51 | */ |
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52 | public class MetricMatchingTraining extends WekaBaseTraining implements ISetWiseTestdataAwareTrainingStrategy { |
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53 | |
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54 | private MetricMatch mm = null; |
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55 | private Classifier classifier = new MetricMatchingClassifier(); |
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56 | |
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57 | private String method; |
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58 | private float threshold; |
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59 | |
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60 | /** |
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61 | * We wrap the classifier here because of classifyInstance with our MetricMatchingClassfier |
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62 | * @return |
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63 | */ |
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64 | @Override |
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65 | public Classifier getClassifier() { |
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66 | return this.classifier; |
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67 | } |
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68 | |
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69 | /** |
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70 | * Set similarity measure method. |
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71 | */ |
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72 | @Override |
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73 | public void setMethod(String method) { |
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74 | this.method = method; |
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75 | } |
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76 | |
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77 | /** |
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78 | * Set threshold for similarity measure. |
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79 | */ |
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80 | @Override |
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81 | public void setThreshold(String threshold) { |
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82 | this.threshold = Float.parseFloat(threshold); |
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83 | } |
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84 | |
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85 | /** |
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86 | * We need the test data instances to do a metric matching, so in this special case we get this data |
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87 | * before evaluation. |
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88 | */ |
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89 | @Override |
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90 | public void apply(SetUniqueList<Instances> traindataSet, Instances testdata) { |
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91 | |
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92 | double score = 0; // matching score to select the best matching training data from the set |
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93 | int num = 0; |
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94 | int biggest_num = 0; |
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95 | MetricMatch tmp; |
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96 | for (Instances traindata : traindataSet) { |
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97 | num++; |
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98 | |
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99 | tmp = new MetricMatch(traindata, testdata); |
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100 | |
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101 | // metric selection may create error, continue to next training set |
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102 | try { |
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103 | tmp.attributeSelection(); |
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104 | tmp.matchAttributes(this.method, this.threshold); |
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105 | }catch(Exception e) { |
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106 | e.printStackTrace(); |
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107 | throw new RuntimeException(e); |
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108 | } |
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109 | |
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110 | // we only select the training data from our set with the most matching attributes |
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111 | if (tmp.getScore() > score && tmp.attributes.size() > 0) { |
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112 | score = tmp.getScore(); |
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113 | this.mm = tmp; |
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114 | biggest_num = num; |
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115 | } |
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116 | } |
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117 | |
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118 | // if we have found a matching instance we use it |
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119 | Instances ilist = null; |
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120 | if (this.mm != null) { |
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121 | ilist = this.mm.getMatchedTrain(); |
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122 | Console.traceln(Level.INFO, "[MATCH FOUND] match: ["+biggest_num +"], score: [" + score + "], instances: [" + ilist.size() + "], attributes: [" + this.mm.attributes.size() + "], ilist attrs: ["+ilist.numAttributes()+"]"); |
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123 | for(Map.Entry<Integer, Integer> attmatch : this.mm.attributes.entrySet()) { |
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124 | Console.traceln(Level.INFO, "[MATCHED ATTRIBUTE] source attribute: [" + this.mm.train.attribute(attmatch.getKey()).name() + "], target attribute: [" + this.mm.test.attribute(attmatch.getValue()).name() + "]"); |
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125 | } |
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126 | }else { |
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127 | Console.traceln(Level.INFO, "[NO MATCH FOUND]"); |
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128 | } |
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129 | |
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130 | // if we have a match we build the MetricMatchingClassifier, if not we fall back to FixClass Classifier |
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131 | try { |
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132 | if(this.mm != null) { |
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133 | this.classifier.buildClassifier(ilist); |
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134 | ((MetricMatchingClassifier) this.classifier).setMetricMatching(this.mm); |
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135 | }else { |
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136 | this.classifier = new FixClass(); |
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137 | this.classifier.buildClassifier(ilist); // this is null, but the FixClass Classifier does not use it anyway |
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138 | } |
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139 | }catch(Exception e) { |
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140 | e.printStackTrace(); |
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141 | throw new RuntimeException(e); |
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142 | } |
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143 | } |
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144 | |
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145 | |
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146 | /** |
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147 | * Encapsulates the classifier configured with WekaBase within but use metric matching. |
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148 | * This allows us to use any Weka classifier with Heterogenous Defect Prediction. |
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149 | */ |
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150 | public class MetricMatchingClassifier extends AbstractClassifier { |
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151 | |
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152 | private static final long serialVersionUID = -1342172153473770935L; |
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153 | private MetricMatch mm; |
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154 | private Classifier classifier; |
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155 | |
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156 | @Override |
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157 | public void buildClassifier(Instances traindata) throws Exception { |
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158 | this.classifier = setupClassifier(); |
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159 | this.classifier.buildClassifier(traindata); |
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160 | } |
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161 | |
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162 | /** |
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163 | * Sets the MetricMatch instance so that we can use matched test data later. |
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164 | * @param mm |
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165 | */ |
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166 | public void setMetricMatching(MetricMatch mm) { |
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167 | this.mm = mm; |
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168 | } |
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169 | |
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170 | /** |
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171 | * Here we can not do the metric matching because we only get one instance. |
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172 | * Therefore we need a MetricMatch instance beforehand to use here. |
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173 | */ |
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174 | public double classifyInstance(Instance testdata) { |
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175 | // get a copy of testdata Instance with only the matched attributes |
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176 | Instance ntest = this.mm.getMatchedTestInstance(testdata); |
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177 | |
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178 | double ret = 0.0; |
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179 | try { |
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180 | ret = this.classifier.classifyInstance(ntest); |
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181 | }catch(Exception e) { |
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182 | e.printStackTrace(); |
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183 | throw new RuntimeException(e); |
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184 | } |
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185 | |
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186 | return ret; |
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187 | } |
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188 | } |
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189 | |
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190 | /** |
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191 | * Encapsulates one MetricMatching process. |
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192 | * One source (train) matches against one target (test). |
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193 | */ |
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194 | public class MetricMatch { |
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195 | Instances train; |
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196 | Instances test; |
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197 | |
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198 | // used to sum up the matching values of all attributes |
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199 | protected double p_sum = 0; |
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200 | |
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201 | // attribute matching, train -> test |
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202 | HashMap<Integer, Integer> attributes = new HashMap<Integer,Integer>(); |
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203 | |
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204 | // used for similarity tests |
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205 | protected ArrayList<double[]> train_values; |
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206 | protected ArrayList<double[]> test_values; |
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207 | |
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208 | |
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209 | public MetricMatch(Instances train, Instances test) { |
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210 | // this is expensive but we need to keep the original data intact |
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211 | this.train = this.deepCopy(train); |
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212 | this.test = test; // we do not need a copy here because we do not drop attributes before the matching and after the matching we create a new Instances with only the matched attributes |
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213 | |
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214 | // convert metrics of testdata and traindata to later use in similarity tests |
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215 | this.train_values = new ArrayList<double[]>(); |
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216 | for (int i = 0; i < this.train.numAttributes(); i++) { |
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217 | if(this.train.classIndex() != i) { |
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218 | this.train_values.add(this.train.attributeToDoubleArray(i)); |
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219 | } |
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220 | } |
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221 | |
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222 | this.test_values = new ArrayList<double[]>(); |
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223 | for (int i=0; i < this.test.numAttributes(); i++) { |
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224 | if(this.test.classIndex() != i) { |
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225 | this.test_values.add(this.test.attributeToDoubleArray(i)); |
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226 | } |
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227 | } |
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228 | } |
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229 | |
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230 | /** |
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231 | * We have a lot of matching possibilities. |
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232 | * Here we try to determine the best one. |
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233 | * |
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234 | * @return double matching score |
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235 | */ |
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236 | public double getScore() { |
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237 | int as = this.attributes.size(); // # of attributes that were matched |
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238 | |
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239 | // we use thresholding ranking approach for numInstances to influence the matching score |
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240 | int instances = this.train.numInstances(); |
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241 | int inst_rank = 0; |
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242 | if(instances > 100) { |
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243 | inst_rank = 1; |
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244 | } |
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245 | if(instances > 500) { |
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246 | inst_rank = 2; |
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247 | } |
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248 | |
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249 | return this.p_sum + as + inst_rank; |
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250 | } |
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251 | |
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252 | public HashMap<Integer, Integer> getAttributes() { |
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253 | return this.attributes; |
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254 | } |
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255 | |
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256 | public int getNumInstances() { |
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257 | return this.train_values.get(0).length; |
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258 | } |
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259 | |
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260 | /** |
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261 | * This creates a new Instance out of the passed Instance and the previously matched attributes. |
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262 | * We do this because the evaluation phase requires an original Instance with every attribute. |
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263 | * |
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264 | * @param test instance |
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265 | * @return new instance |
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266 | */ |
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267 | public Instance getMatchedTestInstance(Instance test) { |
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268 | //create new instance with our matched number of attributes + 1 (the class attribute) |
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269 | Instances testdata = this.getMatchedTest(); |
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270 | |
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271 | Instance ni = new DenseInstance(this.attributes.size()+1); |
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272 | ni.setDataset(testdata); |
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273 | |
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274 | for(Map.Entry<Integer, Integer> attmatch : this.attributes.entrySet()) { |
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275 | ni.setValue(testdata.attribute(attmatch.getKey()), test.value(attmatch.getValue())); |
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276 | } |
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277 | |
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278 | ni.setClassValue(test.value(test.classAttribute())); |
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279 | |
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280 | return ni; |
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281 | } |
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282 | |
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283 | /** |
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284 | * returns a new instances array with the metric matched training data |
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285 | * |
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286 | * @return instances |
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287 | */ |
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288 | public Instances getMatchedTrain() { |
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289 | return this.getMatchedInstances("train", this.train); |
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290 | } |
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291 | |
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292 | /** |
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293 | * returns a new instances array with the metric matched test data |
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294 | * |
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295 | * @return instances |
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296 | */ |
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297 | public Instances getMatchedTest() { |
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298 | return this.getMatchedInstances("test", this.test); |
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299 | } |
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300 | |
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301 | /** |
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302 | * We could drop unmatched attributes from our instances datasets. |
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303 | * Alas, that would not be nice for the following postprocessing jobs and would not work at all for evaluation. |
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304 | * We keep this as a warning for future generations. |
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305 | * |
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306 | * @param name |
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307 | * @param data |
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308 | */ |
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309 | @SuppressWarnings("unused") |
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310 | private void dropUnmatched(String name, Instances data) { |
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311 | for(int i = 0; i < data.numAttributes(); i++) { |
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312 | if(data.classIndex() == i) { |
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313 | continue; |
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314 | } |
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315 | |
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316 | if(name.equals("train") && !this.attributes.containsKey(i)) { |
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317 | data.deleteAttributeAt(i); |
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318 | } |
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319 | |
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320 | if(name.equals("test") && !this.attributes.containsValue(i)) { |
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321 | data.deleteAttributeAt(i); |
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322 | } |
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323 | } |
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324 | } |
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325 | |
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326 | /** |
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327 | * Deep Copy (well, reasonably deep, not sure about header information of attributes) Weka Instances. |
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328 | * |
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329 | * @param data Instances |
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330 | * @return copy of Instances passed |
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331 | */ |
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332 | private Instances deepCopy(Instances data) { |
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333 | Instances newInst = new Instances(data); |
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334 | |
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335 | newInst.clear(); |
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336 | |
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337 | for (int i=0; i < data.size(); i++) { |
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338 | Instance ni = new DenseInstance(data.numAttributes()); |
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339 | for(int j = 0; j < data.numAttributes(); j++) { |
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340 | ni.setValue(newInst.attribute(j), data.instance(i).value(data.attribute(j))); |
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341 | } |
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342 | newInst.add(ni); |
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343 | } |
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344 | |
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345 | return newInst; |
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346 | } |
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347 | |
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348 | /** |
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349 | * Returns a deep copy of passed Instances data for Train or Test data. |
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350 | * It only keeps attributes that have been matched. |
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351 | * |
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352 | * @param name |
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353 | * @param data |
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354 | * @return matched Instances |
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355 | */ |
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356 | private Instances getMatchedInstances(String name, Instances data) { |
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357 | ArrayList<Attribute> attrs = new ArrayList<Attribute>(); |
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358 | |
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359 | // bug attr is a string, really! |
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360 | ArrayList<String> bug = new ArrayList<String>(); |
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361 | bug.add("0"); |
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362 | bug.add("1"); |
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363 | |
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364 | // add our matched attributes and last the bug |
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365 | for(Map.Entry<Integer, Integer> attmatch : this.attributes.entrySet()) { |
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366 | attrs.add(new Attribute(String.valueOf(attmatch.getValue()))); |
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367 | } |
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368 | attrs.add(new Attribute("bug", bug)); |
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369 | |
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370 | // create new instances object of the same size (at least for instances) |
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371 | Instances newInst = new Instances(name, attrs, data.size()); |
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372 | |
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373 | // set last as class |
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374 | newInst.setClassIndex(newInst.numAttributes()-1); |
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375 | |
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376 | // copy data for matched attributes, this depends if we return train or test data |
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377 | for (int i=0; i < data.size(); i++) { |
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378 | Instance ni = new DenseInstance(this.attributes.size()+1); |
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379 | |
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380 | int j = 0; // new indices! |
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381 | for(Map.Entry<Integer, Integer> attmatch : this.attributes.entrySet()) { |
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382 | |
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383 | // test attribute match |
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384 | int value = attmatch.getValue(); |
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385 | |
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386 | // train attribute match |
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387 | if(name.equals("train")) { |
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388 | value = attmatch.getKey(); |
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389 | } |
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390 | |
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391 | ni.setValue(newInst.attribute(j), data.instance(i).value(value)); |
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392 | j++; |
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393 | } |
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394 | ni.setValue(ni.numAttributes()-1, data.instance(i).value(data.classAttribute())); |
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395 | newInst.add(ni); |
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396 | } |
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397 | |
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398 | return newInst; |
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399 | } |
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400 | |
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401 | /** |
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402 | * performs the attribute selection |
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403 | * we perform attribute significance tests and drop attributes |
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404 | * |
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405 | * attribute selection is only performed on the source dataset |
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406 | * we retain the top 15% attributes (if 15% is a float we just use the integer part) |
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407 | */ |
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408 | public void attributeSelection() throws Exception { |
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409 | |
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410 | // it is a wrapper, we may decide to implement ChiSquare or other means of selecting attributes |
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411 | this.attributeSelectionBySignificance(this.train); |
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412 | } |
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413 | |
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414 | private void attributeSelectionBySignificance(Instances which) throws Exception { |
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415 | // Uses: http://weka.sourceforge.net/doc.packages/probabilisticSignificanceAE/weka/attributeSelection/SignificanceAttributeEval.html |
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416 | SignificanceAttributeEval et = new SignificanceAttributeEval(); |
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417 | et.buildEvaluator(which); |
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418 | |
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419 | // evaluate all training attributes |
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420 | HashMap<String,Double> saeval = new HashMap<String,Double>(); |
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421 | for(int i=0; i < which.numAttributes(); i++) { |
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422 | if(which.classIndex() != i) { |
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423 | saeval.put(which.attribute(i).name(), et.evaluateAttribute(i)); |
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424 | } |
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425 | } |
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426 | |
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427 | // sort by significance |
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428 | HashMap<String, Double> sorted = (HashMap<String, Double>) sortByValues(saeval); |
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429 | |
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430 | // Keep the best 15% |
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431 | double last = ((double)saeval.size() / 100.0) * 15.0; |
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432 | int drop_first = saeval.size() - (int)last; |
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433 | |
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434 | // drop attributes above last |
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435 | Iterator<Entry<String, Double>> it = sorted.entrySet().iterator(); |
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436 | while (drop_first > 0) { |
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437 | Map.Entry<String, Double> pair = (Map.Entry<String, Double>)it.next(); |
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438 | if(which.attribute((String)pair.getKey()).index() != which.classIndex()) { |
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439 | which.deleteAttributeAt(which.attribute((String)pair.getKey()).index()); |
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440 | } |
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441 | drop_first-=1; |
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442 | } |
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443 | } |
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444 | |
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445 | /** |
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446 | * Helper method to sort a hashmap by its values. |
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447 | * |
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448 | * @param map |
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449 | * @return sorted map |
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450 | */ |
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451 | private HashMap<String, Double> sortByValues(HashMap<String, Double> map) { |
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452 | List<Map.Entry<String, Double>> list = new LinkedList<Map.Entry<String, Double>>(map.entrySet()); |
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453 | |
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454 | Collections.sort(list, new Comparator<Map.Entry<String, Double>>() { |
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455 | public int compare(Map.Entry<String, Double> o1, Map.Entry<String, Double> o2) { |
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456 | return (o1.getValue()).compareTo( o2.getValue() ); |
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457 | } |
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458 | }); |
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459 | |
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460 | HashMap<String, Double> sortedHashMap = new LinkedHashMap<String, Double>(); |
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461 | for(Map.Entry<String, Double> item : list) { |
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462 | sortedHashMap.put(item.getKey(), item.getValue()); |
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463 | } |
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464 | return sortedHashMap; |
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465 | } |
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466 | |
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467 | |
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468 | /** |
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469 | * Executes the similarity matching between train and test data. |
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470 | * |
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471 | * After this function is finished we have this.attributes with the correct matching between train and test data attributes. |
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472 | * |
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473 | * @param type |
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474 | * @param cutoff |
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475 | */ |
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476 | public void matchAttributes(String type, double cutoff) { |
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477 | |
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478 | MWBMatchingAlgorithm mwbm = new MWBMatchingAlgorithm(this.train.numAttributes(), this.test.numAttributes()); |
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479 | |
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480 | if (type.equals("spearman")) { |
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481 | this.spearmansRankCorrelation(cutoff, mwbm); |
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482 | }else if(type.equals("ks")) { |
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483 | this.kolmogorovSmirnovTest(cutoff, mwbm); |
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484 | }else if(type.equals("percentile")) { |
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485 | this.percentiles(cutoff, mwbm); |
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486 | }else { |
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487 | throw new RuntimeException("unknown matching method"); |
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488 | } |
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489 | |
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490 | // resulting maximal match gets assigned to this.attributes |
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491 | int[] result = mwbm.getMatching(); |
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492 | for( int i = 0; i < result.length; i++) { |
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493 | |
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494 | // -1 means that it is not in the set of maximal matching |
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495 | if( i != -1 && result[i] != -1) { |
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496 | //Console.traceln(Level.INFO, "Found maximal bipartite match between: "+ i + " and " + result[i]); |
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497 | this.attributes.put(i, result[i]); |
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498 | } |
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499 | } |
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500 | } |
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501 | |
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502 | |
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503 | /** |
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504 | * Calculates the Percentiles of the source and target metrics. |
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505 | * |
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506 | * @param cutoff |
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507 | */ |
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508 | public void percentiles(double cutoff, MWBMatchingAlgorithm mwbm) { |
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509 | for( int i = 0; i < this.train.numAttributes(); i++ ) { |
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510 | for( int j = 0; j < this.test.numAttributes(); j++ ) { |
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511 | // negative infinity counts as not present, we do this so we don't have to map between attribute indexes in weka |
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512 | // and the result of the mwbm computation |
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513 | mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY); |
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514 | |
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515 | // class attributes are not relevant |
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516 | if (this.test.classIndex() == j) { |
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517 | continue; |
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518 | } |
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519 | if (this.train.classIndex() == i) { |
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520 | continue; |
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521 | } |
---|
522 | |
---|
523 | // get percentiles |
---|
524 | double train[] = this.train_values.get(i); |
---|
525 | double test[] = this.test_values.get(j); |
---|
526 | |
---|
527 | Arrays.sort(train); |
---|
528 | Arrays.sort(test); |
---|
529 | |
---|
530 | // percentiles |
---|
531 | double train_p; |
---|
532 | double test_p; |
---|
533 | double score = 0.0; |
---|
534 | for( int p=1; p <= 9; p++ ) { |
---|
535 | train_p = train[(int)Math.ceil(train.length * (p/100))]; |
---|
536 | test_p = test[(int)Math.ceil(test.length * (p/100))]; |
---|
537 | |
---|
538 | if( train_p > test_p ) { |
---|
539 | score += test_p / train_p; |
---|
540 | }else { |
---|
541 | score += train_p / test_p; |
---|
542 | } |
---|
543 | } |
---|
544 | |
---|
545 | if( score > cutoff ) { |
---|
546 | this.p_sum += score; |
---|
547 | mwbm.setWeight(i, j, score); |
---|
548 | } |
---|
549 | } |
---|
550 | } |
---|
551 | } |
---|
552 | |
---|
553 | /** |
---|
554 | * Calculate Spearmans rank correlation coefficient as matching score. |
---|
555 | * The number of instances for the source and target needs to be the same so we randomly sample from the bigger one. |
---|
556 | * |
---|
557 | * @param cutoff |
---|
558 | * @param mwbmatching |
---|
559 | */ |
---|
560 | public void spearmansRankCorrelation(double cutoff, MWBMatchingAlgorithm mwbm) { |
---|
561 | double p = 0; |
---|
562 | |
---|
563 | SpearmansCorrelation t = new SpearmansCorrelation(); |
---|
564 | |
---|
565 | // size has to be the same so we randomly sample the number of the smaller sample from the big sample |
---|
566 | if (this.train.size() > this.test.size()) { |
---|
567 | this.sample(this.train, this.test, this.train_values); |
---|
568 | }else if (this.test.size() > this.train.size()) { |
---|
569 | this.sample(this.test, this.train, this.test_values); |
---|
570 | } |
---|
571 | |
---|
572 | // try out possible attribute combinations |
---|
573 | for (int i=0; i < this.train.numAttributes(); i++) { |
---|
574 | for (int j=0; j < this.test.numAttributes(); j++) { |
---|
575 | // negative infinity counts as not present, we do this so we don't have to map between attribute indexs in weka |
---|
576 | // and the result of the mwbm computation |
---|
577 | mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY); |
---|
578 | |
---|
579 | // class attributes are not relevant |
---|
580 | if (this.test.classIndex() == j) { |
---|
581 | continue; |
---|
582 | } |
---|
583 | if (this.train.classIndex() == i) { |
---|
584 | continue; |
---|
585 | } |
---|
586 | |
---|
587 | p = t.correlation(this.train_values.get(i), this.test_values.get(j)); |
---|
588 | if (p > cutoff) { |
---|
589 | this.p_sum += p; |
---|
590 | mwbm.setWeight(i, j, p); |
---|
591 | } |
---|
592 | } |
---|
593 | } |
---|
594 | } |
---|
595 | |
---|
596 | /** |
---|
597 | * Helper method to sample instances for the Spearman rank correlation coefficient method. |
---|
598 | * |
---|
599 | * @param bigger |
---|
600 | * @param smaller |
---|
601 | * @param values |
---|
602 | */ |
---|
603 | private void sample(Instances bigger, Instances smaller, ArrayList<double[]> values) { |
---|
604 | // we want to at keep the indices we select the same |
---|
605 | int indices_to_draw = smaller.size(); |
---|
606 | ArrayList<Integer> indices = new ArrayList<Integer>(); |
---|
607 | Random rand = new Random(); |
---|
608 | while (indices_to_draw > 0) { |
---|
609 | |
---|
610 | int index = rand.nextInt(bigger.size()-1); |
---|
611 | |
---|
612 | if (!indices.contains(index)) { |
---|
613 | indices.add(index); |
---|
614 | indices_to_draw--; |
---|
615 | } |
---|
616 | } |
---|
617 | |
---|
618 | // now reduce our values to the indices we choose above for every attribute |
---|
619 | for (int att=0; att < bigger.numAttributes()-1; att++) { |
---|
620 | |
---|
621 | // get double for the att |
---|
622 | double[] vals = values.get(att); |
---|
623 | double[] new_vals = new double[indices.size()]; |
---|
624 | |
---|
625 | int i = 0; |
---|
626 | for (Iterator<Integer> it = indices.iterator(); it.hasNext();) { |
---|
627 | new_vals[i] = vals[it.next()]; |
---|
628 | i++; |
---|
629 | } |
---|
630 | |
---|
631 | values.set(att, new_vals); |
---|
632 | } |
---|
633 | } |
---|
634 | |
---|
635 | |
---|
636 | /** |
---|
637 | * We run the kolmogorov-smirnov test on the data from our test an traindata |
---|
638 | * if the p value is above the cutoff we include it in the results |
---|
639 | * p value tends to be 0 when the distributions of the data are significantly different |
---|
640 | * but we want them to be the same |
---|
641 | * |
---|
642 | * @param cutoff |
---|
643 | * @return p-val |
---|
644 | */ |
---|
645 | public void kolmogorovSmirnovTest(double cutoff, MWBMatchingAlgorithm mwbm) { |
---|
646 | double p = 0; |
---|
647 | |
---|
648 | KolmogorovSmirnovTest t = new KolmogorovSmirnovTest(); |
---|
649 | for (int i=0; i < this.train.numAttributes(); i++) { |
---|
650 | for ( int j=0; j < this.test.numAttributes(); j++) { |
---|
651 | // negative infinity counts as not present, we do this so we don't have to map between attribute indexs in weka |
---|
652 | // and the result of the mwbm computation |
---|
653 | mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY); |
---|
654 | |
---|
655 | // class attributes are not relevant |
---|
656 | if (this.test.classIndex() == j) { |
---|
657 | continue; |
---|
658 | } |
---|
659 | if (this.train.classIndex() == i) { |
---|
660 | continue; |
---|
661 | } |
---|
662 | |
---|
663 | // this may invoke exactP on small sample sizes which will not terminate in all cases |
---|
664 | //p = t.kolmogorovSmirnovTest(this.train_values.get(i), this.test_values.get(j), false); |
---|
665 | |
---|
666 | // this uses approximateP everytime |
---|
667 | p = t.approximateP(t.kolmogorovSmirnovStatistic(this.train_values.get(i), this.test_values.get(j)), this.train_values.get(i).length, this.test_values.get(j).length); |
---|
668 | if (p > cutoff) { |
---|
669 | this.p_sum += p; |
---|
670 | mwbm.setWeight(i, j, p); |
---|
671 | } |
---|
672 | } |
---|
673 | } |
---|
674 | } |
---|
675 | } |
---|
676 | |
---|
677 | /* |
---|
678 | * Copyright (c) 2007, Massachusetts Institute of Technology |
---|
679 | * Copyright (c) 2005-2006, Regents of the University of California |
---|
680 | * All rights reserved. |
---|
681 | * |
---|
682 | * Redistribution and use in source and binary forms, with or without |
---|
683 | * modification, are permitted provided that the following conditions |
---|
684 | * are met: |
---|
685 | * |
---|
686 | * * Redistributions of source code must retain the above copyright |
---|
687 | * notice, this list of conditions and the following disclaimer. |
---|
688 | * |
---|
689 | * * Redistributions in binary form must reproduce the above copyright |
---|
690 | * notice, this list of conditions and the following disclaimer in |
---|
691 | * the documentation and/or other materials provided with the |
---|
692 | * distribution. |
---|
693 | * |
---|
694 | * * Neither the name of the University of California, Berkeley nor |
---|
695 | * the names of its contributors may be used to endorse or promote |
---|
696 | * products derived from this software without specific prior |
---|
697 | * written permission. |
---|
698 | * |
---|
699 | * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS |
---|
700 | * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT |
---|
701 | * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS |
---|
702 | * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE |
---|
703 | * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, |
---|
704 | * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES |
---|
705 | * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR |
---|
706 | * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) |
---|
707 | * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, |
---|
708 | * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) |
---|
709 | * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED |
---|
710 | * OF THE POSSIBILITY OF SUCH DAMAGE. |
---|
711 | */ |
---|
712 | |
---|
713 | |
---|
714 | |
---|
715 | /** |
---|
716 | * An engine for finding the maximum-weight matching in a complete |
---|
717 | * bipartite graph. Suppose we have two sets <i>S</i> and <i>T</i>, |
---|
718 | * both of size <i>n</i>. For each <i>i</i> in <i>S</i> and <i>j</i> |
---|
719 | * in <i>T</i>, we have a weight <i>w<sub>ij</sub></i>. A perfect |
---|
720 | * matching <i>X</i> is a subset of <i>S</i> x <i>T</i> such that each |
---|
721 | * <i>i</i> in <i>S</i> occurs in exactly one element of <i>X</i>, and |
---|
722 | * each <i>j</i> in <i>T</i> occurs in exactly one element of |
---|
723 | * <i>X</i>. Thus, <i>X</i> can be thought of as a one-to-one |
---|
724 | * function from <i>S</i> to <i>T</i>. The weight of <i>X</i> is the |
---|
725 | * sum, over (<i>i</i>, <i>j</i>) in <i>X</i>, of |
---|
726 | * <i>w<sub>ij</sub></i>. A BipartiteMatcher takes the number |
---|
727 | * <i>n</i> and the weights <i>w<sub>ij</sub></i>, and finds a perfect |
---|
728 | * matching of maximum weight. |
---|
729 | * |
---|
730 | * It uses the Hungarian algorithm of Kuhn (1955), as improved and |
---|
731 | * presented by E. L. Lawler in his book <cite>Combinatorial |
---|
732 | * Optimization: Networks and Matroids</cite> (Holt, Rinehart and |
---|
733 | * Winston, 1976, p. 205-206). The running time is |
---|
734 | * O(<i>n</i><sup>3</sup>). The weights can be any finite real |
---|
735 | * numbers; Lawler's algorithm assumes positive weights, so if |
---|
736 | * necessary we add a constant <i>c</i> to all the weights before |
---|
737 | * running the algorithm. This increases the weight of every perfect |
---|
738 | * matching by <i>nc</i>, which doesn't change which perfect matchings |
---|
739 | * have maximum weight. |
---|
740 | * |
---|
741 | * If a weight is set to Double.NEGATIVE_INFINITY, then the algorithm will |
---|
742 | * behave as if that edge were not in the graph. If all the edges incident on |
---|
743 | * a given node have weight Double.NEGATIVE_INFINITY, then the final result |
---|
744 | * will not be a perfect matching, and an exception will be thrown. |
---|
745 | */ |
---|
746 | class MWBMatchingAlgorithm { |
---|
747 | /** |
---|
748 | * Creates a BipartiteMatcher without specifying the graph size. Calling |
---|
749 | * any other method before calling reset will yield an |
---|
750 | * IllegalStateException. |
---|
751 | */ |
---|
752 | |
---|
753 | /** |
---|
754 | * Tolerance for comparisons to zero, to account for |
---|
755 | * floating-point imprecision. We consider a positive number to |
---|
756 | * be essentially zero if it is strictly less than TOL. |
---|
757 | */ |
---|
758 | private static final double TOL = 1e-10; |
---|
759 | //Number of left side nodes |
---|
760 | int n; |
---|
761 | |
---|
762 | //Number of right side nodes |
---|
763 | int m; |
---|
764 | |
---|
765 | double[][] weights; |
---|
766 | double minWeight; |
---|
767 | double maxWeight; |
---|
768 | |
---|
769 | // If (i, j) is in the mapping, then sMatches[i] = j and tMatches[j] = i. |
---|
770 | // If i is unmatched, then sMatches[i] = -1 (and likewise for tMatches). |
---|
771 | int[] sMatches; |
---|
772 | int[] tMatches; |
---|
773 | |
---|
774 | static final int NO_LABEL = -1; |
---|
775 | static final int EMPTY_LABEL = -2; |
---|
776 | |
---|
777 | int[] sLabels; |
---|
778 | int[] tLabels; |
---|
779 | |
---|
780 | double[] u; |
---|
781 | double[] v; |
---|
782 | |
---|
783 | double[] pi; |
---|
784 | |
---|
785 | List<Integer> eligibleS = new ArrayList<Integer>(); |
---|
786 | List<Integer> eligibleT = new ArrayList<Integer>(); |
---|
787 | |
---|
788 | |
---|
789 | public MWBMatchingAlgorithm() { |
---|
790 | n = -1; |
---|
791 | m = -1; |
---|
792 | } |
---|
793 | |
---|
794 | /** |
---|
795 | * Creates a BipartiteMatcher and prepares it to run on an n x m graph. |
---|
796 | * All the weights are initially set to 1. |
---|
797 | */ |
---|
798 | public MWBMatchingAlgorithm(int n, int m) { |
---|
799 | reset(n, m); |
---|
800 | } |
---|
801 | |
---|
802 | /** |
---|
803 | * Resets the BipartiteMatcher to run on an n x m graph. The weights are |
---|
804 | * all reset to 1. |
---|
805 | */ |
---|
806 | private void reset(int n, int m) { |
---|
807 | if (n < 0 || m < 0) { |
---|
808 | throw new IllegalArgumentException("Negative num nodes: " + n + " or " + m); |
---|
809 | } |
---|
810 | this.n = n; |
---|
811 | this.m = m; |
---|
812 | |
---|
813 | weights = new double[n][m]; |
---|
814 | for (int i = 0; i < n; i++) { |
---|
815 | for (int j = 0; j < m; j++) { |
---|
816 | weights[i][j] = 1; |
---|
817 | } |
---|
818 | } |
---|
819 | minWeight = 1; |
---|
820 | maxWeight = Double.NEGATIVE_INFINITY; |
---|
821 | |
---|
822 | sMatches = new int[n]; |
---|
823 | tMatches = new int[m]; |
---|
824 | sLabels = new int[n]; |
---|
825 | tLabels = new int[m]; |
---|
826 | u = new double[n]; |
---|
827 | v = new double[m]; |
---|
828 | pi = new double[m]; |
---|
829 | |
---|
830 | } |
---|
831 | /** |
---|
832 | * Sets the weight w<sub>ij</sub> to the given value w. |
---|
833 | * |
---|
834 | * @throws IllegalArgumentException if i or j is outside the range [0, n). |
---|
835 | */ |
---|
836 | public void setWeight(int i, int j, double w) { |
---|
837 | if (n == -1 || m == -1) { |
---|
838 | throw new IllegalStateException("Graph size not specified."); |
---|
839 | } |
---|
840 | if ((i < 0) || (i >= n)) { |
---|
841 | throw new IllegalArgumentException("i-value out of range: " + i); |
---|
842 | } |
---|
843 | if ((j < 0) || (j >= m)) { |
---|
844 | throw new IllegalArgumentException("j-value out of range: " + j); |
---|
845 | } |
---|
846 | if (Double.isNaN(w)) { |
---|
847 | throw new IllegalArgumentException("Illegal weight: " + w); |
---|
848 | } |
---|
849 | |
---|
850 | weights[i][j] = w; |
---|
851 | if ((w > Double.NEGATIVE_INFINITY) && (w < minWeight)) { |
---|
852 | minWeight = w; |
---|
853 | } |
---|
854 | if (w > maxWeight) { |
---|
855 | maxWeight = w; |
---|
856 | } |
---|
857 | } |
---|
858 | |
---|
859 | /** |
---|
860 | * Returns a maximum-weight perfect matching relative to the weights |
---|
861 | * specified with setWeight. The matching is represented as an array arr |
---|
862 | * of length n, where arr[i] = j if (i,j) is in the matching. |
---|
863 | */ |
---|
864 | public int[] getMatching() { |
---|
865 | if (n == -1 || m == -1 ) { |
---|
866 | throw new IllegalStateException("Graph size not specified."); |
---|
867 | } |
---|
868 | if (n == 0) { |
---|
869 | return new int[0]; |
---|
870 | } |
---|
871 | ensurePositiveWeights(); |
---|
872 | |
---|
873 | // Step 0: Initialization |
---|
874 | eligibleS.clear(); |
---|
875 | eligibleT.clear(); |
---|
876 | for (Integer i = 0; i < n; i++) { |
---|
877 | sMatches[i] = -1; |
---|
878 | |
---|
879 | u[i] = maxWeight; // ambiguous on p. 205 of Lawler, but see p. 202 |
---|
880 | |
---|
881 | // this is really first run of Step 1.0 |
---|
882 | sLabels[i] = EMPTY_LABEL; |
---|
883 | eligibleS.add(i); |
---|
884 | } |
---|
885 | |
---|
886 | for (int j = 0; j < m; j++) { |
---|
887 | tMatches[j] = -1; |
---|
888 | |
---|
889 | v[j] = 0; |
---|
890 | pi[j] = Double.POSITIVE_INFINITY; |
---|
891 | |
---|
892 | // this is really first run of Step 1.0 |
---|
893 | tLabels[j] = NO_LABEL; |
---|
894 | } |
---|
895 | |
---|
896 | while (true) { |
---|
897 | // Augment the matching until we can't augment any more given the |
---|
898 | // current settings of the dual variables. |
---|
899 | while (true) { |
---|
900 | // Steps 1.1-1.4: Find an augmenting path |
---|
901 | int lastNode = findAugmentingPath(); |
---|
902 | if (lastNode == -1) { |
---|
903 | break; // no augmenting path |
---|
904 | } |
---|
905 | |
---|
906 | // Step 2: Augmentation |
---|
907 | flipPath(lastNode); |
---|
908 | for (int i = 0; i < n; i++) |
---|
909 | sLabels[i] = NO_LABEL; |
---|
910 | |
---|
911 | for (int j = 0; j < m; j++) { |
---|
912 | pi[j] = Double.POSITIVE_INFINITY; |
---|
913 | tLabels[j] = NO_LABEL; |
---|
914 | } |
---|
915 | |
---|
916 | |
---|
917 | |
---|
918 | // This is Step 1.0 |
---|
919 | eligibleS.clear(); |
---|
920 | for (int i = 0; i < n; i++) { |
---|
921 | if (sMatches[i] == -1) { |
---|
922 | sLabels[i] = EMPTY_LABEL; |
---|
923 | eligibleS.add(new Integer(i)); |
---|
924 | } |
---|
925 | } |
---|
926 | |
---|
927 | |
---|
928 | eligibleT.clear(); |
---|
929 | } |
---|
930 | |
---|
931 | // Step 3: Change the dual variables |
---|
932 | |
---|
933 | // delta1 = min_i u[i] |
---|
934 | double delta1 = Double.POSITIVE_INFINITY; |
---|
935 | for (int i = 0; i < n; i++) { |
---|
936 | if (u[i] < delta1) { |
---|
937 | delta1 = u[i]; |
---|
938 | } |
---|
939 | } |
---|
940 | |
---|
941 | // delta2 = min_{j : pi[j] > 0} pi[j] |
---|
942 | double delta2 = Double.POSITIVE_INFINITY; |
---|
943 | for (int j = 0; j < m; j++) { |
---|
944 | if ((pi[j] >= TOL) && (pi[j] < delta2)) { |
---|
945 | delta2 = pi[j]; |
---|
946 | } |
---|
947 | } |
---|
948 | |
---|
949 | if (delta1 < delta2) { |
---|
950 | // In order to make another pi[j] equal 0, we'd need to |
---|
951 | // make some u[i] negative. |
---|
952 | break; // we have a maximum-weight matching |
---|
953 | } |
---|
954 | |
---|
955 | changeDualVars(delta2); |
---|
956 | } |
---|
957 | |
---|
958 | int[] matching = new int[n]; |
---|
959 | for (int i = 0; i < n; i++) { |
---|
960 | matching[i] = sMatches[i]; |
---|
961 | } |
---|
962 | return matching; |
---|
963 | } |
---|
964 | |
---|
965 | /** |
---|
966 | * Tries to find an augmenting path containing only edges (i,j) for which |
---|
967 | * u[i] + v[j] = weights[i][j]. If it succeeds, returns the index of the |
---|
968 | * last node in the path. Otherwise, returns -1. In any case, updates |
---|
969 | * the labels and pi values. |
---|
970 | */ |
---|
971 | int findAugmentingPath() { |
---|
972 | while ((!eligibleS.isEmpty()) || (!eligibleT.isEmpty())) { |
---|
973 | if (!eligibleS.isEmpty()) { |
---|
974 | int i = ((Integer) eligibleS.get(eligibleS.size() - 1)). |
---|
975 | intValue(); |
---|
976 | eligibleS.remove(eligibleS.size() - 1); |
---|
977 | for (int j = 0; j < m; j++) { |
---|
978 | // If pi[j] has already been decreased essentially |
---|
979 | // to zero, then j is already labeled, and we |
---|
980 | // can't decrease pi[j] any more. Omitting the |
---|
981 | // pi[j] >= TOL check could lead us to relabel j |
---|
982 | // unnecessarily, since the diff we compute on the |
---|
983 | // next line may end up being less than pi[j] due |
---|
984 | // to floating point imprecision. |
---|
985 | if ((tMatches[j] != i) && (pi[j] >= TOL)) { |
---|
986 | double diff = u[i] + v[j] - weights[i][j]; |
---|
987 | if (diff < pi[j]) { |
---|
988 | tLabels[j] = i; |
---|
989 | pi[j] = diff; |
---|
990 | if (pi[j] < TOL) { |
---|
991 | eligibleT.add(new Integer(j)); |
---|
992 | } |
---|
993 | } |
---|
994 | } |
---|
995 | } |
---|
996 | } else { |
---|
997 | int j = ((Integer) eligibleT.get(eligibleT.size() - 1)). |
---|
998 | intValue(); |
---|
999 | eligibleT.remove(eligibleT.size() - 1); |
---|
1000 | if (tMatches[j] == -1) { |
---|
1001 | return j; // we've found an augmenting path |
---|
1002 | } |
---|
1003 | |
---|
1004 | int i = tMatches[j]; |
---|
1005 | sLabels[i] = j; |
---|
1006 | eligibleS.add(new Integer(i)); // ok to add twice |
---|
1007 | } |
---|
1008 | } |
---|
1009 | |
---|
1010 | return -1; |
---|
1011 | } |
---|
1012 | |
---|
1013 | /** |
---|
1014 | * Given an augmenting path ending at lastNode, "flips" the path. This |
---|
1015 | * means that an edge on the path is in the matching after the flip if |
---|
1016 | * and only if it was not in the matching before the flip. An augmenting |
---|
1017 | * path connects two unmatched nodes, so the result is still a matching. |
---|
1018 | */ |
---|
1019 | void flipPath(int lastNode) { |
---|
1020 | while (lastNode != EMPTY_LABEL) { |
---|
1021 | int parent = tLabels[lastNode]; |
---|
1022 | |
---|
1023 | // Add (parent, lastNode) to matching. We don't need to |
---|
1024 | // explicitly remove any edges from the matching because: |
---|
1025 | // * We know at this point that there is no i such that |
---|
1026 | // sMatches[i] = lastNode. |
---|
1027 | // * Although there might be some j such that tMatches[j] = |
---|
1028 | // parent, that j must be sLabels[parent], and will change |
---|
1029 | // tMatches[j] in the next time through this loop. |
---|
1030 | sMatches[parent] = lastNode; |
---|
1031 | tMatches[lastNode] = parent; |
---|
1032 | |
---|
1033 | lastNode = sLabels[parent]; |
---|
1034 | } |
---|
1035 | } |
---|
1036 | |
---|
1037 | void changeDualVars(double delta) { |
---|
1038 | for (int i = 0; i < n; i++) { |
---|
1039 | if (sLabels[i] != NO_LABEL) { |
---|
1040 | u[i] -= delta; |
---|
1041 | } |
---|
1042 | } |
---|
1043 | |
---|
1044 | for (int j = 0; j < m; j++) { |
---|
1045 | if (pi[j] < TOL) { |
---|
1046 | v[j] += delta; |
---|
1047 | } else if (tLabels[j] != NO_LABEL) { |
---|
1048 | pi[j] -= delta; |
---|
1049 | if (pi[j] < TOL) { |
---|
1050 | eligibleT.add(new Integer(j)); |
---|
1051 | } |
---|
1052 | } |
---|
1053 | } |
---|
1054 | } |
---|
1055 | |
---|
1056 | /** |
---|
1057 | * Ensures that all weights are either Double.NEGATIVE_INFINITY, |
---|
1058 | * or strictly greater than zero. |
---|
1059 | */ |
---|
1060 | private void ensurePositiveWeights() { |
---|
1061 | // minWeight is the minimum non-infinite weight |
---|
1062 | if (minWeight < TOL) { |
---|
1063 | for (int i = 0; i < n; i++) { |
---|
1064 | for (int j = 0; j < m; j++) { |
---|
1065 | weights[i][j] = weights[i][j] - minWeight + 1; |
---|
1066 | } |
---|
1067 | } |
---|
1068 | |
---|
1069 | maxWeight = maxWeight - minWeight + 1; |
---|
1070 | minWeight = 1; |
---|
1071 | } |
---|
1072 | } |
---|
1073 | |
---|
1074 | @SuppressWarnings("unused") |
---|
1075 | private void printWeights() { |
---|
1076 | for (int i = 0; i < n; i++) { |
---|
1077 | for (int j = 0; j < m; j++) { |
---|
1078 | System.out.print(weights[i][j] + " "); |
---|
1079 | } |
---|
1080 | System.out.println(""); |
---|
1081 | } |
---|
1082 | } |
---|
1083 | } |
---|
1084 | } |
---|