Package org.data2semantics.exp.utils

Examples of org.data2semantics.exp.utils.ResultsTable

78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
        }
      }
    }


    ResultsTable resTable = new ResultsTable();
    resTable.setManWU(0.05);

    boolean inference = true;

    List<EvaluationFunction> evalFuncs1 = new ArrayList<EvaluationFunction>();
    evalFuncs1.add(new Task1ScoreForBins(bins));

    List<EvaluationFunction> evalFuncs2 = new ArrayList<EvaluationFunction>();
    evalFuncs2.add(new Task1Score());
    evalFuncs2.add(new MeanSquaredError());
    evalFuncs2.add(new MeanAbsoluteError());


    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Task1ScoreForBothBins(bins));
    linParms.setDoCrossValidation(false);
    linParms.setSplitFraction((float) 0.8);
    linParms.setEps(0.1);
    linParms.setPs(ps1);

    Map<Double, Double> counts = EvaluationUtils.computeClassCounts(targetBins);
    int[] wLabels = new int[counts.size()];
    double[] weights = new double[counts.size()];

    for (double label : counts.keySet()) {
      wLabels[(int) label - 1] = (int) label;
      weights[(int) label - 1] = 1 / counts.get(label);
    }
    linParms.setWeightLabels(wLabels);
    linParms.setWeights(weights);



    LibLINEARParameters linParms2 = new LibLINEARParameters(LibLINEARParameters.SVR_DUAL, cs);
    linParms2.setEvalFunction(new Task1Score());
    linParms2.setDoCrossValidation(false);
    linParms2.setSplitFraction((float) 0.8);
    linParms2.setEps(0.1);
    linParms2.setPs(ps2);
    linParms2.setBias(1);


    for (int d : depths) {     
      resTable.newRow("ITP BoW, depth="+d);

      RDFFeatureVectorKernel kernel = new RDFIntersectionTreeEdgeVertexPathWithTextKernel(d, false, inference, false);

      System.out.println("Running PathWithText kernel: " + d );

      Map<EvaluationFunction, double[]> resultMap = new HashMap<EvaluationFunction,double[]>();
      Map<EvaluationFunction, double[]> resultMap2 = new HashMap<EvaluationFunction,double[]>();

      List<Result> results = new ArrayList<Result>();

      for (EvaluationFunction evalFunc : evalFuncs1) {
        Result res = new Result();
        double[] resA = new double[seeds.length];
        res.setLabel(evalFunc.getLabel());
        res.setScores(resA);
        res.setHigherIsBetter(evalFunc.isHigherIsBetter());
        results.add(res);
        resultMap.put(evalFunc, resA);
      }

      for (EvaluationFunction evalFunc : evalFuncs2) {
        Result res = new Result();
        double[] resA = new double[seeds.length];
        res.setLabel(evalFunc.getLabel());
        res.setScores(resA);
        res.setHigherIsBetter(evalFunc.isHigherIsBetter());
        results.add(res);
        resultMap2.put(evalFunc, resA);
      }

      Result compR = new Result();
      results.add(compR);


      long tic, toc;

      List<Double> tempLabels = new ArrayList<Double>();
      List<Double> tempLabelsBins = new ArrayList<Double>();
      tempLabels.addAll(target);
      tempLabelsBins.addAll(targetBins);

      tic = System.currentTimeMillis();
      SparseVector[] fv = kernel.computeFeatureVectors(dataset, instances, blackList);
      toc = System.currentTimeMillis();

      fv = TextUtils.computeTFIDF(Arrays.asList(fv)).toArray(new SparseVector[1]);
      fv = KernelUtils.normalize(fv);


      List<SparseVector> fvList = Arrays.asList(fv);


      compR.setLabel("kernel comp time");

      for (int j = 0; j < seeds.length; j++) {
        Collections.shuffle(fvList, new Random(seeds[j]));
        Collections.shuffle(tempLabels, new Random(seeds[j]));
        Collections.shuffle(tempLabelsBins, new Random(seeds[j])); 

        fv = fvList.toArray(new SparseVector[1]);
        double[] targetA = new double[tempLabels.size()];
        double[] targetABins = new double[tempLabelsBins.size()];
        for (int i = 0; i < targetA.length; i++) {
          targetA[i] = tempLabels.get(i);
          targetABins[i] = tempLabelsBins.get(i);
        }

        Prediction[] pred = LibLINEAR.trainTestSplit(fv, targetABins, linParms, linParms.getSplitFraction());     
        Prediction[] pred2 = LibLINEAR.trainTestSplit(fv, targetA, linParms2, linParms2.getSplitFraction());       

        double[] targetSplit = LibLINEAR.splitTestTarget(targetA, linParms.getSplitFraction());

        for (EvaluationFunction ef : evalFuncs1) {
          resultMap.get(ef)[j] = ef.computeScore(targetSplit, pred); 
        }       
        for (EvaluationFunction ef : evalFuncs2) {
          resultMap2.get(ef)[j] = ef.computeScore(targetSplit, pred2); 
        }
      }
      double[] comp = {toc - tic};
      compR.setScores(comp);

      for (Result res : results) {
        resTable.addResult(res);
      }     
    }


    for (int d : depths) {     
      for (int it : iterations) {
        resTable.newRow("RDF WL BoW, depth="+d);

        /*
        List<RDFFeatureVectorKernel> kernels = new ArrayList<RDFFeatureVectorKernel>();
        kernels.add(new RDFWLSubTreeKernel(it,d, inference, false));
        kernels.add(new RDFSimpleTextKernel(d, inference, false));
        RDFFeatureVectorKernel kernel = new RDFCombinedKernel(kernels, true);
        */
       
        RDFFeatureVectorKernel kernel = new RDFWLSubTreeWithTextKernel(it, d, inference, false);
       
       
       
        System.out.println("Running RDFWL + text kernel: " + d + " " + it);

        Map<EvaluationFunction, double[]> resultMap = new HashMap<EvaluationFunction,double[]>();
        Map<EvaluationFunction, double[]> resultMap2 = new HashMap<EvaluationFunction,double[]>();

        List<Result> results = new ArrayList<Result>();

        for (EvaluationFunction evalFunc : evalFuncs1) {
          Result res = new Result();
          double[] resA = new double[seeds.length];
          res.setLabel(evalFunc.getLabel());
          res.setScores(resA);
          res.setHigherIsBetter(evalFunc.isHigherIsBetter());
          results.add(res);
          resultMap.put(evalFunc, resA);
        }

        for (EvaluationFunction evalFunc : evalFuncs2) {
          Result res = new Result();
          double[] resA = new double[seeds.length];
          res.setLabel(evalFunc.getLabel());
          res.setScores(resA);
          res.setHigherIsBetter(evalFunc.isHigherIsBetter());
          results.add(res);
          resultMap2.put(evalFunc, resA);
        }

        Result compR = new Result();
        results.add(compR);


        long tic, toc;

        List<Double> tempLabels = new ArrayList<Double>();
        List<Double> tempLabelsBins = new ArrayList<Double>();
        tempLabels.addAll(target);
        tempLabelsBins.addAll(targetBins);

        tic = System.currentTimeMillis();
        SparseVector[] fv = kernel.computeFeatureVectors(dataset, instances, blackList);
        toc = System.currentTimeMillis();

        fv = TextUtils.computeTFIDF(Arrays.asList(fv)).toArray(new SparseVector[1]);
        fv = KernelUtils.normalize(fv);


        List<SparseVector> fvList = Arrays.asList(fv);


        compR.setLabel("kernel comp time");

        for (int j = 0; j < seeds.length; j++) {
          Collections.shuffle(fvList, new Random(seeds[j]));
          Collections.shuffle(tempLabels, new Random(seeds[j]));
          Collections.shuffle(tempLabelsBins, new Random(seeds[j])); 

          fv = fvList.toArray(new SparseVector[1]);
          double[] targetA = new double[tempLabels.size()];
          double[] targetABins = new double[tempLabelsBins.size()];
          for (int i = 0; i < targetA.length; i++) {
            targetA[i] = tempLabels.get(i);
            targetABins[i] = tempLabelsBins.get(i);
          }

          Prediction[] pred = LibLINEAR.trainTestSplit(fv, targetABins, linParms, linParms.getSplitFraction());     
          Prediction[] pred2 = LibLINEAR.trainTestSplit(fv, targetA, linParms2, linParms2.getSplitFraction());       

          double[] targetSplit = LibLINEAR.splitTestTarget(targetA, linParms.getSplitFraction());

          for (EvaluationFunction ef : evalFuncs1) {
            resultMap.get(ef)[j] = ef.computeScore(targetSplit, pred); 
          }       
          for (EvaluationFunction ef : evalFuncs2) {
            resultMap2.get(ef)[j] = ef.computeScore(targetSplit, pred2); 
          }
        }
        double[] comp = {toc - tic};
        compR.setScores(comp);

        for (Result res : results) {
          resTable.addResult(res);
        } 
      }
    }



    saveResults(resTable, "task1_" + seed + ".ser");
    resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);
    saveResults(resTable.toString(), "task1_" + seed + ".txt");



  }
View Full Code Here
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
    LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
    svmParms.setNumFolds(10);
   
   

    ResultsTable resTable = new ResultsTable();
    resTable.setManWU(0.05);
    resTable.setDigits(2);

    boolean inference = false;
    boolean forward = true;



   
    DTGraph<String,String> sGraph = org.nodes.data.RDF.createDirectedGraph(dataset.getStatements(null, null, null, inference), null, null);
    List<DTNode<String,String>> hubs = SlashBurn.getHubs(sGraph, 1, true);
   
    Comparator<DTNode<String,String>> comp = new SlashBurn.SignatureComparator<String,String>();
    MaxObserver<DTNode<String,String>> obs = new MaxObserver<DTNode<String,String>>(hubs.size(), comp);   
    obs.observe(sGraph.nodes());
   
    List<DTNode<String,String>> degreeHubs = new ArrayList<DTNode<String,String>>(obs.elements());
   
    // Remove hubs from list that are root nodes
    List<DTNode<String,String>> rn = new ArrayList<DTNode<String,String>>();
    Set<String> is = new HashSet<String>();
    for (Resource r : instances) {
      is.add(r.toString());
    }
    for (DTNode<String,String> hub : hubs) {
      if (is.contains(hub.label())) {
        rn.add(hub);
      }
    }
    hubs.removeAll(rn);       
    degreeHubs.removeAll(rn);
   
    System.out.println("Total SB hubs: " + hubs.size());
    System.out.println(hubs); 
    System.out.println(degreeHubs);
   
    for (int i = 0; i < degreeHubs.size() && i < hubs.size(); i++) {
      if (!hubs.get(i).equals(degreeHubs.get(i))) {
        System.out.println(i + " " + hubs.get(i).label() + " " + degreeHubs.get(i).label());
      }
    }
   
   
    /*
    Map<String,Integer> dMap  = GraphUtils.createDegreeHubMap(degreeHubs, 300);
    Map<String,Integer> sbMap = GraphUtils.createHubMap(hubs, 300);
   
    for (String k : dMap.keySet()) {
      int l = dMap.get(k);
      if (sbMap.get(k) != l) {
        System.out.println("fail in level: " + l + " " + sbMap.get(k));
      }
     
    }
    */
   
   
    //int[] hf = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20};
   
    int[] hf = {1,10};

   
   
   
    ///*
    for (int i : depths) {     
      resTable.newRow("RDF WL forward");
      for (int it : iterations) {
        RDFWLSubTreeKernel k = new RDFWLSubTreeKernel(it, i, inference, true, forward, false);
       
        //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
        KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, dataset, instances, target, blackList, evalFuncs);


        System.out.println("Running WL RDF fwd: " + i + " " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        } 
      }
    }
    resTable.addCompResults(resTable.getBestResults());
    //resTable.addCompResults(table2.getBestResults());
    System.out.println(resTable);
   
   
    //*/

    for (int h : hf) {
      for (int i : depths) {     
        resTable.newRow("RDF WL forward Degree " + h);
        for (int it : iterations) {
          RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, i, inference, true, forward);
          k.setHubMap(GraphUtils.createHubMap(degreeHubs, h));

          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, dataset, instances, target, blackList, evalFuncs);


          System.out.println("Running WL RDF fwd Degree: " + i + " " + it + " " + h);
          exp.run();

          for (Result res : exp.getResults()) {
            resTable.addResult(res);
          } 
        }
      }
      resTable.addCompResults(resTable.getBestResults());
      //resTable.addCompResults(table2.getBestResults());
      System.out.println(resTable);
    }
   
   

    ///*
    for (int h : hf) {
      for (int i : depths) {     
        resTable.newRow("RDF WL forward SB " + h);
        for (int it : iterations) {
          RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, i, inference, true, forward);
          k.setHubMap(GraphUtils.createHubMap(hubs, h));

          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, dataset, instances, target, blackList, evalFuncs);


          System.out.println("Running WL RDF fwd SB: " + i + " " + it + " " + h);
          exp.run();

          for (Result res : exp.getResults()) {
            resTable.addResult(res);
          } 
        }
      }
      resTable.addCompResults(resTable.getBestResults());
      //resTable.addCompResults(table2.getBestResults());
      System.out.println(resTable);
    }
   
    //*/


    /*
    for (int h : hf) {
      for (int i : depths) {     
        resTable.newRow("RDF IST SB " + h);
          RDFIntersectionSubTreeSlashBurnKernel k = new RDFIntersectionSubTreeSlashBurnKernel(i, 1, inference, true);
          k.setHubThreshold(h);

          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, dataset, instances, target, blackList, evalFuncs);


          System.out.println("Running RDF IST SB: " + i + " " + h);
          exp.run();

          for (Result res : exp.getResults()) {
            resTable.addResult(res);
          } 
        }
    }
    System.out.println(resTable);
    //*/

   
   
    /*
    for (int i : depths) {     
      for (int it : iterations) {
        resTable.newRow("RDF WL reverse");

        KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(it, i, inference, true, true, false), seeds, linParms, dataset, instances, target, blackList, evalFuncs);

        System.out.println("Running WL RDF rev: " + i + " " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        } 
      }
    }
    System.out.println(resTable);

    for (int i : depths) {     
      for (int it : iterations) {
        resTable.newRow("RDF WL Bi");

        KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(new RDFWLBiSubTreeKernel(it, i, inference, true), seeds, linParms, dataset, instances, target, blackList, evalFuncs);

        System.out.println("Running WL RDF Bi: " + i + " " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        } 
      }
    }
    System.out.println(resTable);
  //*/



    resTable.addCompResults(resTable.getBestResults());
    //resTable.addCompResults(table2.getBestResults());
    System.out.println(resTable);



 
View Full Code Here
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
   

    PropertyPredictionDataSet dataset;
    PropertyPredictionExperiment exp;

    ResultsTable resultsWL  = new ResultsTable();
    ResultsTable resultsSTF = new ResultsTable();
    ResultsTable resultsSTP = new ResultsTable();
    ResultsTable resultsIGW = new ResultsTable();
    ResultsTable resultsIGP = new ResultsTable();

    Experimenter experimenter = new Experimenter(NUMBER_OF_PROC);
    Thread expT = new Thread(experimenter);
    expT.setDaemon(true);
    expT.start();



    try {
      for (GeneralPredictionDataSetParameters params : dataSetsParams) {
        dataset = DataSetFactory.createPropertyPredictionDataSet(params);
        //dataset.removeSmallClasses(5);
        dataset.setLabels(labels);
        //dataset.removeVertexAndEdgeLabels();

       
       
        resultsWL.newRow(dataset.getLabel() + " WLSubTreeKernel");
        for (int i = 0; i < 4; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "WL" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new WLSubTreeKernel(i), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsWL.addResult(exp.getResults().getAccuracy());
            resultsWL.addResult(exp.getResults().getF1());
          }
        }


       
        resultsSTF.newRow(dataset.getLabel() + " IntersectionFullSubTree");
        for (int i = 0; i < 4; i++) {

          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionFullSubTree" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionSubTreeKernel(i, 1), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsSTF.addResult(exp.getResults().getAccuracy());
            resultsSTF.addResult(exp.getResults().getF1());
          }
        }

        resultsSTP.newRow(dataset.getLabel() + " IntersectionPartialSubTree");
        for (int i = 0; i < 4; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionPartialSubTree" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionPartialSubTreeKernel(i, 0.01), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsSTP.addResult(exp.getResults().getAccuracy());
            resultsSTP.addResult(exp.getResults().getF1());
          }
        }

        //*/


       
        resultsIGP.newRow(dataset.getLabel() + " IntersectionGraphPath");
        for (int i = 1; i < 3; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionGraphPath" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionGraphPathKernel(i, 1), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsIGP.addResult(exp.getResults().getAccuracy());
            resultsIGP.addResult(exp.getResults().getF1());
          }
        }       

        resultsIGW.newRow(dataset.getLabel() + " IntersectionGraphWalk");
        for (int i = 1; i < 3; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionGraphWalk" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionGraphWalkKernel(i, 1), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsIGW.addResult(exp.getResults().getAccuracy());
            resultsIGW.addResult(exp.getResults().getF1());
          }
        }
        //*/       

      }
    } catch (Exception e) {
      e.printStackTrace();
    }

    experimenter.stop();

    while (expT.isAlive()) {
      try {
        Thread.sleep(1000);
      } catch (Exception e) {
        e.printStackTrace();
      }
    }

    try {
      int fileId = (int) (Math.random() * 100000000); 
      File file = new File(DATA_DIR + fileId + "_" + "all_results" + ".txt");
      PrintWriter fileOut = new PrintWriter(new FileOutputStream(file));

      List<Result> bestResults = new ArrayList<Result>();
     
      bestResults = resultsWL.getBestResults(bestResults);
      bestResults = resultsSTF.getBestResults(bestResults);
      bestResults = resultsSTP.getBestResults(bestResults);
      bestResults = resultsIGW.getBestResults(bestResults);
      bestResults = resultsIGP.getBestResults(bestResults);
     
      resultsWL.addCompResults(bestResults);
      resultsSTF.addCompResults(bestResults);
      resultsSTP.addCompResults(bestResults);
      resultsIGW.addCompResults(bestResults);
      resultsIGP.addCompResults(bestResults);
           
     
      fileOut.println(resultsWL);
      fileOut.println(resultsSTF);
      fileOut.println(resultsSTP);
      fileOut.println(resultsIGW);
      fileOut.println(resultsIGP);

      fileOut.println(resultsWL.allScoresToString());
      fileOut.println(resultsSTF.allScoresToString());
      fileOut.println(resultsSTP.allScoresToString());
      fileOut.println(resultsIGW.allScoresToString());
      fileOut.println(resultsIGP.allScoresToString());

      System.out.println(resultsWL);
      System.out.println(resultsSTF);
      System.out.println(resultsSTP);
      System.out.println(resultsIGW);
      System.out.println(resultsIGP);

      System.out.println(resultsWL.allScoresToString());
      System.out.println(resultsSTF.allScoresToString());
      System.out.println(resultsSTP.allScoresToString());
      System.out.println(resultsIGW.allScoresToString());
      System.out.println(resultsIGP.allScoresToString());
     
      fileOut.close();


    } catch (Exception e) {
View Full Code Here
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
    int[] depths = {1, 2, 3};
    int[] iterations = {0, 2, 4, 6};
    dataset = new RDFFileDataSet(dataDir, RDFFormat.NTRIPLES);

    ResultsTable resTable = new ResultsTable();
    resTable.setManWU(0.05);
    resTable.setDigits(3);

    boolean inference = true;


    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());

   
   
    for (int i : depths) { 
      resTable.newRow("WL RDF, depth="+i); 
      for (int it : iterations) {

        List<List<Result>> res = new ArrayList<List<Result>>();
        for (long seed : seeds) {
          long[] seeds2 = {seed};
         
          createGeoDataSet((int)(1000 * fraction), fraction, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasTheme");
          List<Double> target = EvaluationUtils.createTarget(labels);

          LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
          linParms.setDoCrossValidation(true);
          linParms.setNumFolds(5);

          Map<Double, Double> counts = EvaluationUtils.computeClassCounts(target);
          int[] wLabels = new int[counts.size()];
          double[] weights = new double[counts.size()];

          for (double label : counts.keySet()) {
            wLabels[(int) label - 1] = (int) label;
            weights[(int) label - 1] = 1 / counts.get(label);
          }
          linParms.setWeightLabels(wLabels);
          linParms.setWeights(weights);

          RDFLinearKernelExperiment exp = new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(it, i, inference, true), seeds2, linParms, dataset, instances, target, blackList, evalFuncs);

          System.out.println("Running WL RDF: " + i + " " + it);
          exp.setDoCV(true);
          exp.run();
          res.add(exp.getResults());
        }

        for (Result res2 : Result.mergeResultLists(res)) {
          resTable.addResult(res2);
        }
      }
    }
    System.out.println(resTable);


    for (int i : depths) { 
      resTable.newRow("WL RDF BoW, depth="+i); 
      for (int it : iterations) {

        List<List<Result>> res = new ArrayList<List<Result>>();
        for (long seed : seeds) {
          long[] seeds2 = {seed};
         
          createGeoDataSet((int)(1000 * fraction), fraction, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasTheme");
          List<Double> target = EvaluationUtils.createTarget(labels);

          LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
          linParms.setDoCrossValidation(true);
          linParms.setNumFolds(5);

          Map<Double, Double> counts = EvaluationUtils.computeClassCounts(target);
          int[] wLabels = new int[counts.size()];
          double[] weights = new double[counts.size()];

          for (double label : counts.keySet()) {
            wLabels[(int) label - 1] = (int) label;
            weights[(int) label - 1] = 1 / counts.get(label);
          }
          linParms.setWeightLabels(wLabels);
          linParms.setWeights(weights);

          RDFLinearKernelExperiment exp = new RDFLinearKernelExperiment(new RDFWLSubTreeWithTextKernel(it, i, inference, true), seeds2, linParms, dataset, instances, target, blackList, evalFuncs);

          System.out.println("Running WL RDF with text: " + i + " " + it);
          exp.setDoCV(true);
          exp.run();
          res.add(exp.getResults());
        }

        for (Result res2 : Result.mergeResultLists(res)) {
          resTable.addResult(res2);
        }
      }
    }
    System.out.println(resTable);
   
   
    for (int i : depths) { 
      resTable.newRow("ITP, depth="+i); 

      List<List<Result>> res = new ArrayList<List<Result>>();
      for (long seed : seeds) {
        long[] seeds2 = {seed};
        createGeoDataSet((int)(1000 * fraction), fraction, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasTheme");
        List<Double> target = EvaluationUtils.createTarget(labels);

        LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
        linParms.setDoCrossValidation(true);
        linParms.setNumFolds(5);

        Map<Double, Double> counts = EvaluationUtils.computeClassCounts(target);
        int[] wLabels = new int[counts.size()];
        double[] weights = new double[counts.size()];

        for (double label : counts.keySet()) {
          wLabels[(int) label - 1] = (int) label;
          weights[(int) label - 1] = 1 / counts.get(label);
        }
        linParms.setWeightLabels(wLabels);
        linParms.setWeights(weights);

        RDFLinearKernelExperiment exp = new RDFLinearKernelExperiment(new RDFIntersectionTreeEdgeVertexPathKernel(i, false, inference, true), seeds2, linParms, dataset, instances, target, blackList, evalFuncs);

        System.out.println("Running EVP: " + i);
        exp.setDoCV(true);
        exp.run();
        res.add(exp.getResults());
      }

      for (Result res2 : Result.mergeResultLists(res)) {
        resTable.addResult(res2);
      }
    }
    System.out.println(resTable);
   
    for (int i : depths) { 
      resTable.newRow("ITP BoW, depth="+i); 

      List<List<Result>> res = new ArrayList<List<Result>>();
      for (long seed : seeds) {
        long[] seeds2 = {seed};
        createGeoDataSet((int)(1000 * fraction), fraction, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasTheme");
        List<Double> target = EvaluationUtils.createTarget(labels);

        LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
        linParms.setDoCrossValidation(true);
        linParms.setNumFolds(5);

        Map<Double, Double> counts = EvaluationUtils.computeClassCounts(target);
        int[] wLabels = new int[counts.size()];
        double[] weights = new double[counts.size()];

        for (double label : counts.keySet()) {
          wLabels[(int) label - 1] = (int) label;
          weights[(int) label - 1] = 1 / counts.get(label);
        }
        linParms.setWeightLabels(wLabels);
        linParms.setWeights(weights);

        RDFLinearKernelExperiment exp = new RDFLinearKernelExperiment(new RDFIntersectionTreeEdgeVertexPathWithTextKernel(i, false, inference, false), seeds2, linParms, dataset, instances, target, blackList, evalFuncs);

        System.out.println("Running EVP with text: " + i);
        exp.setDoCV(true);
        exp.setDoTFIDF(true);
        exp.run();
        res.add(exp.getResults());
      }

      for (Result res2 : Result.mergeResultLists(res)) {
        resTable.addResult(res2);
      }
    }
    System.out.println(resTable);

   

    for (int i : depths) { 
      resTable.newRow("IST, depth="+i); 

      List<List<Result>> res = new ArrayList<List<Result>>();
      for (long seed : seeds) {
        long[] seeds2 = {seed};
        createGeoDataSet((int)(1000 * fraction), fraction, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasTheme");
        List<Double> target = EvaluationUtils.createTarget(labels);

        LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
        svmParms.setNumFolds(5);


        KernelExperiment<RDFGraphKernel> exp = new RDFOldKernelExperiment(new RDFIntersectionSubTreeKernel(i, 1, inference, true), seeds2, svmParms, dataset, instances, labels, blackList);

        System.out.println("Running IST: " + i);
        exp.run();
        res.add(exp.getResults());
      }

      for (Result res2 : Result.mergeResultLists(res)) {
        resTable.addResult(res2);
      }
    }
    System.out.println(resTable); 

    saveResults(resTable, "geo_theme_DMoLD.ser");

    resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);
    saveResults(resTable.toString(), "geo_theme_DMoLD_.txt");
  }
View Full Code Here
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
    int depth = 3;
    int iteration = 6;
    boolean inference = true;

    LibSVMParameters parms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
    ResultsTable resTable = new ResultsTable();

    resTable.newRow("WL RDF");
    for (double frac : fractions) {

      Result res = new Result();
      res.setLabel("runtime");
      for (long seed : seeds) {

        createAffiliationPredictionDataSet(frac, seed);

        KernelExperiment<RDFGraphKernel> exp = new RDFKernelRunTimeExperiment(new ECML2013RDFWLSubTreeKernel(iteration, depth, inference, true, false), seeds, parms, dataset, instances, labels, blackList);

        System.out.println("Running WL RDF: " + frac);
        exp.run();
        res.addResult(exp.getResults().get(0));
      }
      resTable.addResult(res);
    }

    resTable.newRow("IST");
    for (double frac : fractions) {

      Result res = new Result();
      res.setLabel("runtime");
      for (long seed : seeds) {

        createAffiliationPredictionDataSet(frac, seed);

        KernelExperiment<RDFGraphKernel> exp = new RDFKernelRunTimeExperiment(new RDFIntersectionSubTreeKernel(depth, 1, inference, true, false), seeds, parms, dataset, instances, labels, blackList);

        System.out.println("Running IST: " + frac);
        exp.run();
        res.addResult(exp.getResults().get(0));
      }

      resTable.addResult(res);
    }


    long tic, toc;



    resTable.newRow("WL");
    for (double frac : fractions) {

      Result res = new Result();
      res.setLabel("runtime");
      for (long seed : seeds) {

        createAffiliationPredictionDataSet(frac,seed);
        tic = System.currentTimeMillis();
        PropertyPredictionDataSet ds = DataSetFactory.createPropertyPredictionDataSet(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 3, false, true));
        toc = System.currentTimeMillis();

        KernelExperiment<GraphKernel> exp = new GraphKernelRunTimeExperiment(new ECML2013WLSubTreeKernel(iteration), seeds, parms, ds.getGraphs(), labels);

        System.out.println("Running WL: " + frac);
        exp.run();
        res.addResult(exp.getResults().get(0));

        double[] comps = {2 * (toc-tic) + res.getScore()};
        Result resC = new Result(comps,"comp time 2"); 
        res.addResult(resC);
      }

      resTable.addResult(res);
    }
    /*
    resTable.newRow("");
    for (double frac : fractions) {
      createAffiliationPredictionDataSet(frac);
      tic = System.currentTimeMillis();
      PropertyPredictionDataSet ds = DataSetFactory.createPropertyPredictionDataSet(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 3, false, true));
      toc = System.currentTimeMillis();


      KernelExperiment<GraphKernel> exp = new GraphKernelExperiment(new ECML2013IntersectionGraphPathKernel(2,1), seeds, parms, ds.getGraphs(), labels);

      System.out.println("Running IGP: " + frac);
      exp.run();

      double[] comps =  {0,0};
  comps[0] = 2*(toc-tic) + exp.getResults().get(exp.getResults().size()-1).getScore();
      comps[1] = 2*(toc-tic) + exp.getResults().get(exp.getResults().size()-1).getScore();
          Result resC = new Result(comps,"comp time 2"); 
      exp.getResults().get(exp.getResults().size()-1).addResult(resC);

      resTable.addResult(exp.getResults().get(exp.getResults().size()-1));
    }*/


    resTable.newRow("IGW");
    for (double frac : fractions) {

      Result res = new Result();
      res.setLabel("runtime");
      for (long seed : seeds) {
        createAffiliationPredictionDataSet(frac,seed);
        tic = System.currentTimeMillis();
        PropertyPredictionDataSet ds = DataSetFactory.createPropertyPredictionDataSet(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 3, false, true));
        toc = System.currentTimeMillis();

        KernelExperiment<GraphKernel> exp = new GraphKernelRunTimeExperiment(new ECML2013IntersectionGraphWalkKernel(2,1), seeds, parms, ds.getGraphs(), labels);

        System.out.println("Running IGW: " + frac);
        exp.run();

        res.addResult(exp.getResults().get(0));

        double[] comps = {2 * (toc-tic) + res.getScore()};
        Result resC = new Result(comps,"comp time 2"); 
        res.addResult(resC);
      }

      resTable.addResult(res);
    }


    //resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);
    saveResults(resTable.toString(), "affiliation_runningtime.txt");


  }
View Full Code Here
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
    createGeoDataSet(1, 1, 10, "http://data.bgs.ac.uk/ref/Lexicon/hasLithogenesis");

    LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
    svmParms.setNumFolds(10);
   
    ResultsTable resTable = new ResultsTable();
    resTable.setDigits(2);

    for (int depth : depths) {
      resTable.newRow("WL RDF, depth="+depth);
      for (int it : iterations) {
        RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFWLSubTreeKernel(it, depth, inference, true), seeds, svmParms, dataset, instances, labels, blackList);

       
        System.out.println("Running WL RDF: " + depth + " " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }
      }
    }
    System.out.println(resTable);

    for (int depth : depths) {
      resTable.newRow("WL RDF BoW, depth="+depth);
      for (int it : iterations) {
        RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFWLSubTreeWithTextKernel(it, depth, inference, true), seeds, svmParms, dataset, instances, labels, blackList);

       
        System.out.println("Running WL RDF with Text: " + depth + " " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }
      }
    }
    System.out.println(resTable);
   
   
    ResultsTable table2 = new ResultsTable();
   
    for (int depth : depths) {
      resTable.newRow("ITP, depth="+depth);
      table2.newRow("");
     
      RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFIntersectionTreeEdgeVertexPathKernel(depth, false, inference, true), seeds, svmParms, dataset, instances, labels, blackList);

      System.out.println("Running ITP: " + depth);
      exp.run();

      for (Result res : exp.getResults()) {
        resTable.addResult(res);
        table2.addResult(res);
      }
    }
    System.out.println(resTable);
   
    for (int depth : depths) {
      resTable.newRow("ITP BoW, depth="+depth);
      table2.newRow("");
     
      RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFIntersectionTreeEdgeVertexPathWithTextKernel(depth, false, inference, false), seeds, svmParms, dataset, instances, labels, blackList);

      System.out.println("Running ITP with Text: " + depth);
      exp.run();

      for (Result res : exp.getResults()) {
        resTable.addResult(res);
        table2.addResult(res);
      }
    }
    System.out.println(resTable);
   
    for (int depth : depths) {
      resTable.newRow("IST, depth="+depth);
      RDFOldKernelExperiment exp = new RDFOldKernelExperiment(new RDFIntersectionSubTreeKernel(depth, 1, inference, true), seeds, svmParms, dataset, instances, labels, blackList);

      System.out.println("Running IST: " + depth);
      exp.run();

      for (Result res : exp.getResults()) {
        resTable.addResult(res);
      }
    }
   
    resTable.addCompResults(resTable.getBestResults());
    resTable.addCompResults(table2.getBestResults());
    System.out.println(resTable);

  }
View Full Code Here
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446

    LibSVMParameters parms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
    //parms.setEvalFunction(LibSVMParameters.F1);

    ResultsTable resTable = new ResultsTable();

   

    boolean inference = false;
    for (int i = 1; i <= depth; i++) {
      resTable.newRow("WL RDF, no inference, depth=" + i);
      for (int it : iterations) {
        ECML2013RDFWLSubTreeKernel k = new ECML2013RDFWLSubTreeKernel(it, i, inference, true, blankLabels);
               
        KernelExperiment<RDFGraphKernel> exp = new RDFOldKernelExperiment(k, seeds, parms, dataset, instances, labels, blackList);

        System.out.println("Running WL RDF: " + i + " " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }
      }
    }
    saveResults(resTable, "affiliation.ser");



    inference = true;
    for (int i = 1; i <= depth; i++) {
      resTable.newRow("WL RDF, inference, depth=" + i);
      for (int it : iterations) {
        ECML2013RDFWLSubTreeKernel k = new ECML2013RDFWLSubTreeKernel(it, i, inference, true, blankLabels);
               
        KernelExperiment<RDFGraphKernel> exp = new RDFOldKernelExperiment(k, seeds, parms, dataset, instances, labels, blackList);

        System.out.println("Running WL RDF: " + i + " " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }
      }
    }
    saveResults(resTable, "affiliation.ser");


    inference = false;
    for (int i = 1; i <= depth; i++) {
      resTable.newRow("IST, no inference, depth=" + i);
      KernelExperiment<RDFGraphKernel> exp = new RDFOldKernelExperiment(new RDFIntersectionSubTreeKernel(i, 1, inference, true, blankLabels), seeds, parms, dataset, instances, labels, blackList);

      System.out.println("Running IST: " + i + " ");
      exp.run();

      for (Result res : exp.getResults()) {
        resTable.addResult(res);
      }
    }
    saveResults(resTable, "affiliation.ser");

    inference = true;
    for (int i = 1; i <= depth; i++) {
      resTable.newRow("IST, inference, depth=" + i);
      KernelExperiment<RDFGraphKernel> exp = new RDFOldKernelExperiment(new RDFIntersectionSubTreeKernel(i, 1, inference, true, blankLabels), seeds, parms, dataset, instances, labels, blackList);

      System.out.println("Running IST: " + i + " ");
      exp.run();

      for (Result res : exp.getResults()) {
        resTable.addResult(res);
      }
    }
    saveResults(resTable, "affiliation.ser");


    inference = false;
    for (int i = 1; i <= depth; i++) {
      resTable.newRow("IPST, no inference, depth=" + i);
      KernelExperiment<RDFGraphKernel> exp = new RDFOldKernelExperiment(new RDFIntersectionPartialSubTreeKernel(i, 0.01, inference, true, blankLabels), seeds, parms, dataset, instances, labels, blackList);

      System.out.println("Running IPST: " + i + " ");
      exp.run();

      for (Result res : exp.getResults()) {
        resTable.addResult(res);
      }
    }
    saveResults(resTable, "affiliation.ser");

    inference = true;
    for (int i = 1; i <= depth; i++) {
      resTable.newRow("IPST, inference, depth=" + i);
      KernelExperiment<RDFGraphKernel> exp = new RDFOldKernelExperiment(new RDFIntersectionPartialSubTreeKernel(i, 0.01, inference, true, blankLabels), seeds, parms, dataset, instances, labels, blackList);

      System.out.println("Running IPST: " + i + " ");
      exp.run();

      for (Result res : exp.getResults()) {
        resTable.addResult(res);
      }
    }
    saveResults(resTable, "affiliation.ser");




    List<GeneralPredictionDataSetParameters> dataSetsParams = new ArrayList<GeneralPredictionDataSetParameters>();

    dataSetsParams.add(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 1, false, false));
    dataSetsParams.add(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 2, false, false));
    dataSetsParams.add(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 3, false, false));

    dataSetsParams.add(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 1, false, true));
    dataSetsParams.add(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 2, false, true));
    dataSetsParams.add(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 3, false, true));


    int[] iterationsIG = {1,2};
    long tic, toc;

    for (GeneralPredictionDataSetParameters params : dataSetsParams) {
      tic = System.currentTimeMillis();
      PropertyPredictionDataSet ds = DataSetFactory.createPropertyPredictionDataSet(params);
      toc = System.currentTimeMillis();

      if (blankLabels) {
        ds.removeVertexAndEdgeLabels();
      }

      resTable.newRow("WL");
      for (int it : iterations) {
        KernelExperiment<GraphKernel> exp = new GraphKernelExperiment(new ECML2013WLSubTreeKernel(it), seeds, parms, ds.getGraphs(), labels);

        System.out.println("Running WL: " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }

        double[] comps =  {0,0};
        comps[0] = toc-tic;
        comps[1] = toc-tic;
        Result resC = new Result(comps,"comp time 2");
        resTable.addResult(resC);

      }
    }
    saveResults(resTable, "affiliation.ser");



    /*
    dataSetsParams = new ArrayList<GeneralPredictionDataSetParameters>();

    dataSetsParams.add(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 1, false, false));
    dataSetsParams.add(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 2, false, false));

    dataSetsParams.add(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 1, false, true));
    dataSetsParams.add(new GeneralPredictionDataSetParameters(dataset, blackLists, instances, 2, false, true));
     */


    for (GeneralPredictionDataSetParameters params : dataSetsParams) {
      tic = System.currentTimeMillis();
      PropertyPredictionDataSet ds = DataSetFactory.createPropertyPredictionDataSet(params);
      toc = System.currentTimeMillis();

      if (blankLabels) {
        ds.removeVertexAndEdgeLabels();
      }

      resTable.newRow("IGP");
      for (int it : iterationsIG) {
        KernelExperiment<GraphKernel> exp = new GraphKernelExperiment(new ECML2013IntersectionGraphPathKernel(it,1), seeds, parms, ds.getGraphs(), labels);

        System.out.println("Running IGP: " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }

        double[] comps =  {0,0};
        comps[0] = toc-tic;
        comps[1] = toc-tic;
        Result resC = new Result(comps,"comp time 2");
        resTable.addResult(resC);
      }
    }
    saveResults(resTable, "affiliation.ser");


   
    for (GeneralPredictionDataSetParameters params : dataSetsParams) {
      tic = System.currentTimeMillis();
      PropertyPredictionDataSet ds = DataSetFactory.createPropertyPredictionDataSet(params);
      toc = System.currentTimeMillis();

      if (blankLabels) {
        ds.removeVertexAndEdgeLabels();
      }

      resTable.newRow("IGW");
      for (int it : iterationsIG) {
        KernelExperiment<GraphKernel> exp = new GraphKernelExperiment(new ECML2013IntersectionGraphWalkKernel(it,1), seeds, parms, ds.getGraphs(), labels);

        System.out.println("Running IGW: " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }

        double[] comps =  {0,0};
        comps[0] = toc-tic;
        comps[1] = toc-tic;
        Result resC = new Result(comps,"comp time 2");
        resTable.addResult(resC);

      }
    }
   
   
    saveResults(resTable, "affiliation.ser");


    resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);
    saveResults(resTable.toString(), "affiliation" + blankLabels + ".txt");

  }
View Full Code Here
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());


    ResultsTable resTable = new ResultsTable();
    resTable.setDigits(2);
    //resTable.setManWU(0.05);
   
   
    ///*
    for (int it : iterations) {
      resTable.newRow("WL, it: " + it);
      MoleculeGraphExperiment<UGraph<String>> exp = new MoleculeGraphExperiment<UGraph<String>>(new WLUSubTreeKernel(it, true), seeds, svmParms, graphs, labels, evalFuncs);

      System.out.println("Running WL, it: " + it);
      exp.run();

      for (Result res : exp.getResults()) {
        resTable.addResult(res);
      }
    }
    System.out.println(resTable);
    //*/

    /*
    for (int it : iterations2) {
      resTable.newRow("WL separate RDF, it: " + it);
      MoleculeLinearGraphExperiment<DTGraph<String,String>> exp = new MoleculeLinearGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, true), seeds, linParms, rdfGraphs, labels, evalFuncs);

      System.out.println("Running WL separate RDF, it: " + it);
      exp.run();

      for (Result res : exp.getResults()) {
        resTable.addResult(res);
      }
    }
    System.out.println(resTable);
    //*/

   
    for (int d = 1; d < 4; d++) {
      for (int it : iterations2) {   
        resTable.newRow("WL RDF, " + d + ", " + it);
       
        RDFWLSubTreeKernel k = new RDFWLSubTreeKernel(it, d, false, true, true, false);
       
        RDFGraphKernelExperiment exp = new RDFGraphKernelExperiment(k, seeds, svmParms, ts, instances, labels, new ArrayList<Statement>(), evalFuncs);

        System.out.println("Running WL RDF, it: " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }
      }
      System.out.println(resTable);
    }


    for (int d = 1; d < 4; d++) {
      resTable.newRow("WL RDF type, d " + d);
      for (int it : iterations2) {   

        RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, d, false, true, true);
        k.setHubMap(GraphUtils.createRDFTypeHubMap(ts, false));

        RDFGraphKernelExperiment exp = new RDFGraphKernelExperiment(k, seeds, svmParms, ts, instances, labels, new ArrayList<Statement>(), evalFuncs);

        System.out.println("Running WL RDF, type it: " + it);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }
      }
      System.out.println(resTable);
    }
    //*/

    int[] hf = {0,1,2,3,4,5,6,7};

    for (int h : hf) {
      for (int i : depths) {     
        resTable.newRow("RDF WL NonSig " + h);
        for (int it : iterations2) {
          RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, i, false, true, true);
          k.setHubMap(GraphUtils.createNonSigHubMap(nonSigDegreeHubs, h));

          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, ts, instances, labels, new ArrayList<Statement>(), evalFuncs);


          System.out.println("Running WL RDF NonSig: " + i + " " + it + " " + h);
          exp.run();

          for (Result res : exp.getResults()) {
            resTable.addResult(res);
          } 
        }
      }
      System.out.println(resTable);
    }
   
    for (int h : hf) {
      for (int i : depths) {     
        resTable.newRow("RDF WL SB " + h);
        for (int it : iterations2) {
          RDFWLSubTreeSlashBurnKernel k = new RDFWLSubTreeSlashBurnKernel(it, i, false, true, true);
          k.setHubMap(GraphUtils.createHubMap(hubs, h));

          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, ts, instances, labels, new ArrayList<Statement>(), evalFuncs);


          System.out.println("Running WL RDF SB: " + i + " " + it + " " + h);
          exp.run();

          for (Result res : exp.getResults()) {
            resTable.addResult(res);
          } 
        }
      }
      System.out.println(resTable);
    }


    /*
    for (int h : hf) {
      for (int i : depths) {     
        resTable.newRow("RDF IST SB " + h);
          RDFIntersectionSubTreeSlashBurnKernel k = new RDFIntersectionSubTreeSlashBurnKernel(i, 1, false, true);
          k.setHubThreshold(h);

          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(k, seeds, linParms, dataset, instances, target, blackList, evalFuncs);
          KernelExperiment<RDFGraphKernel> exp = new RDFGraphKernelExperiment(k, seeds, svmParms, ts, instances, labels, new ArrayList<Statement>(), evalFuncs);


          System.out.println("Running RDF IST SB: " + i + " " + h);
          exp.run();

          for (Result res : exp.getResults()) {
            resTable.addResult(res);
          } 
        }
    }
    System.out.println(resTable);
     */



    resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);

  }
 
View Full Code Here
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
    svmParms.setWeights(EvaluationUtils.computeWeights(target));
    //---------

    //---------
    // Results Table
    ResultsTable resTable = new ResultsTable();
    resTable.setDigits(3);
    //---------

    //-------
    //Data graph, with the label information
    List<Statement> allStmts3 = GraphUtils.getStatements4Depth(dataset, instances, 4, false);
    List<Statement> allStmts4;
    if (fullGraph) {
      allStmts4 = dataset.getStatements(null, null, null, false);
    } else {
      allStmts4 = GraphUtils.getStatements4Depth(dataset, instances, 5, false);
    }

    allStmts3.removeAll(blackList);
    allStmts4.removeAll(blackList);
    DTGraph<String,String> graph3 = org.nodes.data.RDF.createDirectedGraph(allStmts3, null, null); //used to generate instances
    DTGraph<String,String> graph4 = org.nodes.data.RDF.createDirectedGraph(allStmts4, null, null); //Used to find hubs
    System.out.println("Total nodes d3: " + graph3.nodes().size() + ", total nodes d4: " + graph4.nodes().size());

    List<DTNode<String,String>> instanceNodes3 = new ArrayList<DTNode<String,String>>();
    List<DTNode<String,String>> instanceNodes4 = new ArrayList<DTNode<String,String>>();
    for (Resource i : instances) {
      instanceNodes3.add(graph3.node(i.toString()));
      instanceNodes4.add(graph4.node(i.toString()));
    }
    //--------


    //--------
    // Get the different hub lists
    int maxHubs = 1000;

    // RDF.Type hubs
    List<DTNode<String,String>> RDFTypeHubs = GraphUtils.getTypeHubs(graph4);

    // Regular Degree
    Comparator<Node<String>> compRegDeg = new DegreeComparator<String>();
    MaxObserver<Node<String>> obsRegDeg = new MaxObserver<Node<String>>(maxHubs + instances.size(), compRegDeg);
    obsRegDeg.observe(graph4.nodes());
    List<DTNode<String,String>> regDegreeHubs = new ArrayList<DTNode<String,String>>();
    for (Node<String> n : obsRegDeg.elements()) {
      regDegreeHubs.add((DTNode<String,String>) n);
    }

    // Signature Degree
    Comparator<DTNode<String,String>> compSigDeg = new SlashBurn.SignatureComparator<String,String>();
    MaxObserver<DTNode<String,String>> obsSigDeg = new MaxObserver<DTNode<String,String>>(maxHubs + instances.size(), compSigDeg);       
    obsSigDeg.observe(graph4.nodes());
    List<DTNode<String,String>> sigDegreeHubs = new ArrayList<DTNode<String,String>>(obsSigDeg.elements());

    // Informed Degree
    List<Integer> classes = new ArrayList<Integer>();
    for (double d : target) {
      classes.add((int) d);
    }
    Classified<DTNode<String, String>> classified = Classification.combine(instanceNodes4, classes);

    InformedAvoidance ia = new InformedAvoidance(graph4, classified, 4); 

    Comparator<DTNode<String, String>> compUnInformed = ia.uninformedComparator(4);
    MaxObserver<DTNode<String,String>> obsUnInformed = new MaxObserver<DTNode<String,String>>(maxHubs + instances.size(), compUnInformed);
    obsUnInformed.observe(graph4.nodes());
    List<DTNode<String,String>> unInformedDegreeHubs = new ArrayList<DTNode<String,String>>(obsUnInformed.elements());

    Iterator<DTNode<String, String>> ite = unInformedDegreeHubs.iterator();
    while(ite.hasNext())
      if(! ia.viableHub(ite.next(), 4, 4))
        ite.remove();

    Comparator<DTNode<String, String>> compInformed = ia.informedComparator(4);
    MaxObserver<DTNode<String,String>> obsInformed = new MaxObserver<DTNode<String,String>>(maxHubs + instances.size(), compInformed);
    obsInformed.observe(graph4.nodes());
    List<DTNode<String,String>> informedDegreeHubs = new ArrayList<DTNode<String,String>>(obsInformed.elements());

    ite = informedDegreeHubs.iterator();
    while(ite.hasNext())
      if(! ia.viableHub(ite.next(), 4, 4))
        ite.remove();

    // Remove hubs from list that are root nodes
    List<DTNode<String,String>> rn = new ArrayList<DTNode<String,String>>();
    Set<String> is = new HashSet<String>();
    for (Resource r : instances) {
      is.add(r.toString());
    }
    for (DTNode<String,String> n : graph4.nodes()) {
      if (is.contains(n.label())) {
        rn.add(n);
      }
    }
    RDFTypeHubs.removeAll(rn);
    regDegreeHubs.removeAll(rn);
    sigDegreeHubs.removeAll(rn);
    unInformedDegreeHubs.removeAll(rn);
    informedDegreeHubs.removeAll(rn);

    List<List<DTNode<String,String>>> hubLists = new ArrayList<List<DTNode<String,String>>>();
    hubLists.add(RDFTypeHubs);
    hubLists.add(regDegreeHubs);
    hubLists.add(sigDegreeHubs);
    //hubLists.add(unInformedDegreeHubs);
    //hubLists.add(informedDegreeHubs);


    boolean forward = true;
    int it = 6;
    int depth = 3;
    int[] hubThs = {0,1,2,3,4,5,10,20,30,40,50,100};
    //int[] hubThs = {};

    MoleculeGraphExperiment<DTGraph<String,String>> exp;

    /*
    int[] iterations = {0,1,2,3,4,5,6};

    for (int i : iterations) {
      resTable.newRow("Baseline: " + i);
      List<DTNode<String,String>> newIN = new ArrayList<DTNode<String,String>>(instanceNodes3);

      exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(i, true, forward),
          seeds, svmParms, GraphUtils.getSubGraphs(graph3, newIN, depth), target, evalFuncs);

      System.out.println("running baseline, it: " + i);
      exp.run();

      for (Result res : exp.getResults()) {
        resTable.addResult(res);
      }
    }
    System.out.println(resTable);
     */

    for (int th : hubThs) {
      resTable.newRow("Hub Threshold: " + th);

      for (List<DTNode<String,String>> hubList : hubLists) {

        List<List<DTNode<String,String>>> newIN = new ArrayList<List<DTNode<String,String>>>();
        List<DTGraph<String,String>> newGs = GraphUtils.simplifyGraph3Way(graph3, GraphUtils.createHubMap(hubList, th), instanceNodes3, newIN);

        ///*
        //List<DTNode<String,String>> newIN = new ArrayList<DTNode<String,String>>(instanceNodes3);
        //DTGraph<String,String> newG = GraphUtils.simplifyGraph(graph3, GraphUtils.createHubMap(hubList, th), newIN, false, true);
        //System.out.println("New #links: "+ newG.numLinks() + ", old #links: " + graph3.numLinks());

        exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward),
            seeds, svmParms, GraphUtils.getSubGraphs(newGs.get(0), newIN.get(0), depth), target, evalFuncs);

        System.out.println("running, remove hubs, th: " + th);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }

        //newIN = new ArrayList<DTNode<String,String>>(instanceNodes3);
        //newG = GraphUtils.simplifyGraph(graph3, GraphUtils.createHubMap(hubList, th), newIN, true, false);
        //System.out.println("New #links: "+ newG.numLinks() + ", old #links: " + graph3.numLinks());

        exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward),
            seeds, svmParms, GraphUtils.getSubGraphs(newGs.get(1), newIN.get(1), depth), target, evalFuncs);

        System.out.println("running, relabel hubs, th: " + th);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }

        //newIN = new ArrayList<DTNode<String,String>>(instanceNodes3);
        //newG = GraphUtils.simplifyGraph(graph3, GraphUtils.createHubMap(hubList, th), newIN, true, true);
        //System.out.println("New #links: "+ newG.numLinks() + ", old #links: " + graph3.numLinks());

        exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward),
            seeds, svmParms, GraphUtils.getSubGraphs(newGs.get(2), newIN.get(2), depth), target, evalFuncs);

        System.out.println("running, relabel+remove hubs, th: " + th);
        exp.run();

        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }
        //*/

      }
      System.out.println(resTable);
    }

    resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);   
    System.out.println(resTable.allScoresToString());

    saveResults(resTable.toString(), "results_simp_" + System.currentTimeMillis() + ".txt");
    saveResults(resTable.allScoresToString(), "results_full_simp_" + System.currentTimeMillis() + ".txt");

/*
* INSTANCE EXTRACTION!!!!! ah yeah ;)
*
*/
   
    // Discover average size
    List<DTGraph<String,String>> sg = GraphUtils.getSubGraphs(graph3, new ArrayList<DTNode<String,String>>(instanceNodes3), 3);
    double avg = 0;
    for (DTGraph<String,String> sgp : sg) {
      avg += sgp.size();
    }
    avg /= sg.size();
    System.out.println("Average Number of nodes: " + avg);
   
    // Results Table
    ResultsTable resTable2 = new ResultsTable();
    resTable2.setDigits(3);

    double[] fracs = {0.25, 0.5, 0.75, 1.0, 1.5, 2.0};

    for (double frac : fracs) {
      resTable2.newRow("Fraction: " + frac);
     
      List<DTGraph<String,String>> ihDepth = InstanceHelper.getInstances(graph4, instanceNodes4, target, InstanceHelper.Method.DEPTH, (int) Math.round(frac*avg), 4, true); 
     
      exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward), seeds, svmParms, ihDepth, target, evalFuncs);
     
      System.out.println("running, Depth: " + frac);
      exp.run();

      for (Result res : exp.getResults()) {
        resTable2.addResult(res);
      }
     
      List<DTGraph<String,String>> ihUnInformed = InstanceHelper.getInstances(graph4, instanceNodes4, target, InstanceHelper.Method.UNINFORMED, (int) Math.round(frac*avg), 4, true);
     
      exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward), seeds, svmParms, ihUnInformed, target, evalFuncs);
     
      System.out.println("running, UnInformed: " + frac);
      exp.run();

      for (Result res : exp.getResults()) {
        resTable2.addResult(res);
      }
     
      List<DTGraph<String,String>> ihInformed = InstanceHelper.getInstances(graph4, instanceNodes4, target, InstanceHelper.Method.INFORMED, (int) Math.round(frac*avg), 4, true);

      exp = new MoleculeGraphExperiment<DTGraph<String,String>>(new WLSubTreeKernel(it, true, forward), seeds, svmParms, ihInformed, target, evalFuncs);
     
      System.out.println("running, Informed: " + frac);
      exp.run();

      for (Result res : exp.getResults()) {
        resTable2.addResult(res);
      }
      System.out.println(resTable2); 
    }
   
    resTable2.addCompResults(resTable2.getBestResults());
    System.out.println(resTable2);   
    System.out.println(resTable2.allScoresToString());

    saveResults(resTable2.toString(), "results_ie_" + System.currentTimeMillis() + ".txt");
    saveResults(resTable2.allScoresToString(), "results_full_ie_" + System.currentTimeMillis() + ".txt");
  }
View Full Code Here
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261

    PropertyPredictionDataSet dataset;
    PropertyPredictionExperiment exp;

    ResultsTable resultsWL  = new ResultsTable();
    ResultsTable resultsSTF = new ResultsTable();
    ResultsTable resultsSTP = new ResultsTable();
    ResultsTable resultsIGW = new ResultsTable();
    ResultsTable resultsIGP = new ResultsTable();

    Experimenter experimenter = new Experimenter(NUMBER_OF_PROC);
    Thread expT = new Thread(experimenter);
    expT.setDaemon(true);
    expT.start();



    try {
      for (BinaryPropertyPredictionDataSetParameters params : dataSetsParams) {
        dataset = DataSetFactory.createPropertyPredictionDataSet(params);
        dataset.removeSmallClasses(5);
        dataset.setLabels(labels);
        //dataset.removeVertexAndEdgeLabels();

        resultsWL.newRow(dataset.getLabel() + " WLSubTreeKernel");

        for (int i = 0; i < 4; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "WL" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new WLSubTreeKernel(i), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsWL.addResult(exp.getResults().getAccuracy());
            resultsWL.addResult(exp.getResults().getF1());
           
            System.out.println("Running WL, it " + i + " on " + dataset.getLabel());
          }
        }

       
        resultsSTF.newRow(dataset.getLabel() + " IntersectionFullSubTree");
        for (int i = 0; i < 3; i++) {

          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionFullSubTree" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionSubTreeKernel(i, 1), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsSTF.addResult(exp.getResults().getAccuracy());
            resultsSTF.addResult(exp.getResults().getF1());
           
            System.out.println("Running STF, it " + i + " on " + dataset.getLabel());
          }
        }

        resultsSTP.newRow(dataset.getLabel() + " IntersectionPartialSubTree");
        for (int i = 0; i < 3; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionPartialSubTree" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionPartialSubTreeKernel(i, 0.01), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsSTP.addResult(exp.getResults().getAccuracy());
            resultsSTP.addResult(exp.getResults().getF1());
           
            System.out.println("Running STP, it " + i + " on " + dataset.getLabel());
          }
        }


       
        resultsIGP.newRow(dataset.getLabel() + " IntersectionGraphPath");
        for (int i = 1; i < 3; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionGraphPath" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionGraphPathKernel(i, 1), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsIGP.addResult(exp.getResults().getAccuracy());
            resultsIGP.addResult(exp.getResults().getF1());
           
            System.out.println("Running IGP, it " + i + " on " + dataset.getLabel());
          }
        }       

        resultsIGW.newRow(dataset.getLabel() + " IntersectionGraphWalk");
        for (int i = 1; i < 3; i++) {
          if (experimenter.hasSpace()) {   
            int fileId = (int) (Math.random() * 100000000); 
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionGraphWalk" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionGraphWalkKernel(i, 1), seeds, cs, maxClassSize, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsIGW.addResult(exp.getResults().getAccuracy());
            resultsIGW.addResult(exp.getResults().getF1());
           
            System.out.println("Running IGW, it " + i + " on " + dataset.getLabel());
          }
        }
        //*/


      }
    } catch (Exception e) {
      e.printStackTrace();
    }

    experimenter.stop();

    while (expT.isAlive()) {
      try {
        Thread.sleep(1000);
      } catch (Exception e) {
        e.printStackTrace();
      }
    }

    try {
      int fileId = (int) (Math.random() * 100000000); 
      File file = new File(DATA_DIR + fileId + "_" + "all_results" + ".txt");
      PrintWriter fileOut = new PrintWriter(new FileOutputStream(file));

      List<Result> bestResults = new ArrayList<Result>();
     
      bestResults = resultsWL.getBestResults(bestResults);
      bestResults = resultsSTF.getBestResults(bestResults);
      bestResults = resultsSTP.getBestResults(bestResults);
      bestResults = resultsIGW.getBestResults(bestResults);
      bestResults = resultsIGP.getBestResults(bestResults);
     
      resultsWL.addCompResults(bestResults);
      resultsSTF.addCompResults(bestResults);
      resultsSTP.addCompResults(bestResults);
      resultsIGW.addCompResults(bestResults);
      resultsIGP.addCompResults(bestResults);
           
     
      fileOut.println(resultsWL);
      fileOut.println(resultsSTF);
      fileOut.println(resultsSTP);
      fileOut.println(resultsIGW);
      fileOut.println(resultsIGP);

      fileOut.println(resultsWL.allScoresToString());
      fileOut.println(resultsSTF.allScoresToString());
      fileOut.println(resultsSTP.allScoresToString());
      fileOut.println(resultsIGW.allScoresToString());
      fileOut.println(resultsIGP.allScoresToString());

      System.out.println(resultsWL);
      System.out.println(resultsSTF);
      System.out.println(resultsSTP);
      System.out.println(resultsIGW);
      System.out.println(resultsIGP);

      System.out.println(resultsWL.allScoresToString());
      System.out.println(resultsSTF.allScoresToString());
      System.out.println(resultsSTP.allScoresToString());
      System.out.println(resultsIGW.allScoresToString());
      System.out.println(resultsIGP.allScoresToString());


    } catch (Exception e) {
      e.printStackTrace();
    }
View Full Code Here
TOP

Related Classes of org.data2semantics.exp.utils.ResultsTable

  • org.apache.commons.math3.stat.inference.MannWhitneyUTest
  • org.apache.commons.math3.stat.inference.TTest
  • org.data2semantics.exp.AffiliationExperiment
  • org.data2semantics.exp.CommitteeMemberCompareExperiment
  • org.data2semantics.exp.CompareLinearKernelsExperiment
  • org.data2semantics.exp.dmold.DMoLDAffiliationExperiment
  • org.data2semantics.exp.dmold.DMoLDGeoExperiment
  • org.data2semantics.exp.dmold.DMoLDRunningTimeExperiment
  • org.data2semantics.exp.dmold.DMoLDTask2Experiment
  • org.data2semantics.exp.dmold.DMoLDThemeExperiment
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.